In multicast networks especially with multiple multicast sources where bottleneck effects may occur frequently , frequently used multicast service leads to inefficient network utilization problems . This paper presents a new network utilization algorithm for multicasting called load distribution algorithm ( LDA ) . The LDA algorithm uses selecting candidate path based on ant colony algorithm and multicast scheduling to distribute the contention multicast packets onto their corresponding candidate paths . Convergence proof of all algorithms is given using an appropriate cost function and discussing about its initial conditions . Application of the new IFR on feature extraction from facial image sequences is given in two steps : i ) image preprocessing , which includes normalization , histogram equalization , mean centering and background removal , ii ) adaptive LDA feature estimation . In the preprocessing stage , all input images are cropped and prepared for the next step . In addition to a traditional perplexity measure , I discuss how to use cosine similarity and Symmetric Kullback-Leibler Divergence to evaluate clustering performance . Three examples of using LDA outputs as building blocks for more complicated machine learning system are also demonstrated : 1 ) Cascaded LDA for taxonomy building . 2 ) In-cluster similarity computing . These transformed signals are decomposed into 8 IMFs . Significant features are extracted from the signals and classified by ANN and LDA method and tabulated . From the results , we found that IMF4 provides the most significant classification accuracies . subsection : Foley - Sammon Transform The learned null Foley - Sammon transform ( NFST ) space is closely related to linear discriminant analysis ( LDA ) , also known as Foley - Sammon transform ( FST ) . So before we formulate NFST , let us first briefly revisit FST . Due to the use of a log - log scale , even small improvements in ( log - average ) MR correspond to large reductions in false - positives . On INRIA , our ( slightly modified ) baseline version of ACF scores at 17.3 % MR compared to 17.0 % MR for the model reported in [ 7 ] . 4 Detection with Oblique Splits ( ACF - LDA ) In this section we modify the ACF detector to enable oblique splits and report the resulting gains . Table 1 , rows 2 - 4 , shows the results of ACF with oblique splits , updated every T boosting iterations 2http: // vision.ucsd.edu / ˜pdollar / toolbox / doc / ( denoted by ACF - LDA - T ) . While more frequent updates improve accuracy , ACF - LDA - 16 has negligibly higher MR than ACF - LDA - 4 but a nearly fourfold reduction in training time ( timed using 12 cores ) . Training the brute force version of ACF - LDA , updated at every iteration and each tree split ( 7 interior nodes per depth - 3 tree ) would have taken about 5 · 4 · 7 = 140 hours . For these results we used regularization of = .1 and patch size of m = 5 ( effect of varying m is explored in § 6 ) . Most similar to our loss function , Weinberger propose to learn a distance metric for large margin nearest neighbor classification . Kostinger further explore a similar LDA based objective . More recently Schroff , building on Sun , introduced the triplet loss for face recognition . We increased the number of FBANKs from 24 to 40 , which yielded slightly lower PER . We also evaluated the standard Kaldi features — 39 dimensional MFCCs spliced by a context window of 7 , followed by LDA and MLLT transform and with feature - space speaker - dependent MLLR , which were the same features used in the HMM - DNN baseline in Table [ reference ] . The well - engineered features improved the accuracy of our system by more than 1 % absolute . SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks , including the object recognition problem . ICA is a feature extraction technique which can be considered a generalization of principal component analysis ( PCA ) . ICA has been mainly used on the problem of blind signal separation . However , these estimated components oftentimes have higher order dependencies , such as co-activation of components , that are not accounted for in the model . Topographic independent component analysis(TICA ) , a modification of ICA , takes into account higher order dependencies and orders components topographically as a function of dependence . Here , we aim to visualize the time course of TICA basis activations to movie stimuli . The invention relates to a ICA ( Independent Component Analysis ) shrinkage de-noising method evaluating noise variance evaluation based on a wavelet packet , relating to a structural response signal de-noising and processing technology . The method comprises the following steps of : firstly , evaluating the noise variance by using a wavelet packet ; secondly , carrying out shrinkage estimation by using an ICA shrinkage theory for de-noising ; and finally , reconstructing a signal through ICA inverse transformation . The method can be used for better eliminating the noise in the signal , can also express the superiority in a high-noise environment and can be used for de-noising signals in the fields of civil engineering , aerospace , automatic control , mechanical engineering , bridge engineering , water conservancy projects and the like . In this paper , we show that minimization of the statistical dependence using broadband independent component analysis ( ICA ) can be successfully exploited for acoustic source localization . As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths , the ICA criterion offers a theoretically more rigorous framework than conventional techniques based on an idealized single-path and single-source signal model . This leads to algorithms which outperform other localization methods , especially in the presence of multiple simultaneously active sound sources and under adverse conditions , notably in reverberant environments . The simulation is performed by using the ORL database , where the face images vary in illumination , expression , pose , and scale . The results show that the PICF method using the corresponding similarity measurement has better retrieving performance than classical PCA or ICA method with the usual measurement , and the two-stage-accuracy can reach 97.5 % Training such a generative network that maps latent variable to a sample does not in theory require a discriminator network as in GANs , or approximate inference as in variational autoencoders . Indeed , if is bijective , it can be trained through maximum likelihood using the change of variable formula : This formula has been discussed in several papers including the maximum likelihood formulation of independent components analysis ( ICA ) bell1995information , hyvarinen2004independent , gaussianization NIPS1994_901 , chen2000gaussianization and deep density models bengio1991artificial , rippel2013high , dinh2014nice , balle2015density . As the existence proof of nonlinear ICA solutions hyvarinen1999nonlinear suggests , auto - regressive models can be seen as tractable instance of maximum likelihood nonlinear ICA , where the residual corresponds to the independent components . However , naive application of the change of variable formula produces models which are computationally expensive and poorly conditioned , and so large scale models of this type have not entered general use . Nevertheless , little effort has been devoted to address this problem ( see Table [ reference ] for a comparison of meta - parameters required by unsupervised feature learning methods ) . To the best of our knowledge , work in this direction includes ICA and sparse filtering . Although ICA provides good results at object recognition tasks , the method scales poorly to large datasets and high input dimensionality . Commonly used algorithms for unsupervised feature learning include Restricted Boltzmann Machines ( RBM ) , auto - encoders , sparse coding and hybrids such as PSD . Many other methods such as ICA , Reconstruction ICA ( RICA ) , Sparse Filtering and methods related to vector quantization such as Orthogonal Matching Pursuit ( OMP - k ) have also been used in the literature to extract unsupervised feature representations . These algorithms could be divided into two categories : explicitly modeling or not the input distribution . Kipf and Welling report their mean GCN and ICA results on the same data splits over 100 runs with random weight initializations . The other baseline methods are taken from Yang et al . . First , a comprehensive evaluation index system of logistics service providers selection is put forward in the paper . Then , In order to overcome the lack of previous evaluation methods , projection pursuit method based on genetic algorithm for the uncertain and high-dimensional nature of the evaluation indicator data of logistics service suppliers is suggest whose essence is a dimensionality reduction technology in order to meet the purpose of analyzing high-dimensional and non-linear data in the low-dimensional space . The model can determine the index objective weights result directly from sample data of candidate schemes themselves , and can avoid the subjective negative impact on conclusion in subjective evaluation methods . Without supervised information , current deep learning methods are difficult to be directly applied to image clustering problems . Image clustering needs to deal with three main problems : 1 ) the curse of dimensionality caused by high-dimensional image data ; 2 ) extracting the effective image features ; 3 ) combining feature extraction , dimensionality reduction and clustering . In this paper , we propose a new clustering framework called Deep Embedded Dimensionality Reduction Clustering ( DERC ) via Probability-Based Triplet Loss , which effectively solves the above issues . Multiple correspondence analysis is a dimension reduction technique which plays a large role in the analysis of tables with categorical nominal variables , such as survey data . Though it is usually motivated and derived using geometric considerations , we prove that in fact , it can be seen as a single proximal Newton step of a natural bilinear exponential family model for categorical data : the multinomial logit bilinear model . The central idea of the proposed representation takes root in the Whitney embedding theorem which guarantees an embedding of a one dimensional manifold in as a space curve . The resulting of such dimension reduction , is a simplification of comparing two behaviors to that of comparing two curves in R3 . This comparison is additionally theoretically and numerically easier to implement for statistical analysis . In this work , we propose a new idea which we named Multi-class Fukunaga Koontz Discriminant Analysis ( FKDA ) by incorporating the Fukunaga Koontz Transform within the optimization for maximizing class separation criteria in LDA , UDP , and LPP . In contrast to traditional LDA , UDP , and LPP , our approach can work with very high dimensional data as input , without requiring a separate dimensionality reduction step to make the scatter matrices full rank . In addition , the FKDA formulation seeks optimal projection direction vectors that are orthogonal which the existing methods can not guarantee , and it has the capability of finding the exact solutions to the " trace ratio " objective in discriminant analysis problems while traditional methods can only deal with a relaxed and inexact " ratio trace " objective . Selections of records can be made and examined , and dependencies between fields can be investigated in detail . MADEN was used as a tool for investigating and assessing many data processing algorithms , firstly data-reducing ( clustering ) methods , then dimensionality-reducing techniques . These included a new ' directed ' form of principal components analysis , several novel applications of artificial neural networks , and discriminant analysis techniques which illustrated how groups within a database can be separated . We introduce GEPIA ( Gene Expression Profiling Interactive Analysis ) , a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data . GEPIA provides key interactive and customizable functions including differential expression analysis , profiling plotting , correlation analysis , patient survival analysis , similar gene detection and dimensionality reduction analysis . The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas , scientific discussion and the therapeutic discovery process . According to the features of human finger vein image , a finger vein feature extraction method based on dimensionality reduction idea is put forward . In this method , the vein feature extraction is carried out through searching for valley shape region that is formed by the vein of two dimension gray image on one dimension gray curve . However , processing this kind of streaming data on-line is a hard problem . This paper address the use of a dimensionality reduction perspective to classify streaming data and present a preliminary work for its adaptation to new data in changeable environments . This new extended proposal allows from a computational point of view the treatment of the new data without any penalty and evolving the previously analyzed data . Unsupervised learning methods offer one promising avenue for acquiring a concise representation of the state with a good similarity metric . This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section . However , current techniques fail to satisfactorily define and optimize a reasonable objective required for scalable unsupervised feature learning in networks . Classic approaches based on linear and non - linear dimensionality reduction techniques such as Principal Component Analysis , Multi - Dimensional Scaling and their extensions optimize an objective that transforms a representative data matrix of the network such that it maximizes the variance of the data representation . Consequently , these approaches invariably involve eigendecomposition of the appropriate data matrix which is expensive for large real - world networks . Unsupervised feature learning approaches typically exploit the spectral properties of various matrix representations of graphs , especially the Laplacian and the adjacency matrices . Under this linear algebra perspective , these methods can be viewed as dimensionality reduction techniques . Several linear ( e.g. , PCA ) and non - linear ( e.g. , IsoMap ) dimensionality reduction techniques have been proposed . 3DMM has been widely applied in numerous areas , such as computer vision , graphics , human behavioral analysis and craniofacial surgery . 3DMM is learnt through supervision by performing dimension reduction , normally Principal Component Analysis ( PCA ) , on a training set of face images / scans . To model highly variable 3D face shapes , a large amount of high - quality 3D face scans is required . This might prove to be helpful in systems for detecting relatively small objects . We have studied how factorizing convolutions and aggressive dimension reductions inside neural network can result in networks with relatively low computational cost while maintaining high quality . The combination of lower parameter count and additional regularization with batch - normalized auxiliary classifiers and label - smoothing allows for training high quality networks on relatively modest sized training sets . However , the computational complexity of 3D search typically grows cubically with respect to resolution and becomes too expensive for large scenes or real - time applications such as autonomous driving . Instead , in this work , we reduce the search space following the dimension reduction principle : we take the advantage of mature 2D object detectors ( Fig . [ reference ] ) . Covariance pooling was initially used in for pooling covariance matrix from the outputs of CNNs . proposed an alternative to compute second - order statistics in the setting of CNNs . However , such two works do not use either dimensionality reduction layers or non - linear rectification layers for second - order statistics . In this paper , we present a strong motivation for exploring them in the context of facial expression recognition . Finally , the integrated memory is passed into a bi - directional LSTM , and the output will captures the interaction among the context words and the query words : In multiple layers , the integrated hierarchical matching module can be regarded as the input of next layer after a dimensionality reduction processing . We call this memory networks of full - orientation matching . [ reference ] is the FCN ( also FCN - Basic ) decoding technique . The important design element of the FCN model is dimensionality reduction step of the encoder feature maps . This compresses the encoder feature maps which are then used in the corresponding decoders . DM-based CAD ( CADDM ) and DBT-based CAD ( CADDBT ) were previously developed by our group . The major differences between the CAD systems include : ( a ) CADDBT uses two parallel processes whereas CADDM uses a single process for enhancing MCs and removing the structured background , ( b ) CADDBT has additional processing steps to reduce the false positives ( FPs ) , including ranking of candidates of cluster seeds and cluster members and the use of adaptive CNR and size thresholds at clustering and FP reduction , ( c ) CADDM uses convolution neural network ( CNN ) and linear discriminant analysis ( LDA ) to differentiate true microcalcifications from FPs based on their morphological and CNN features . The performance difference is assessed by FROC analysis using test set ( 100 views with MCs and 74 views without MCs ) independent of their respective training sets . To demonstrate the effect of CRD on differentiating malignant from benign masses and the potential improvement to the diagnostic accuracy of breast cancer using ultrasound , this chapter evaluates the diagnostic accuracy of different classifiers when the CRD was added to five powerful mass features obtained from previous studies including one geometric feature : Depth-Width ratio ( DW ) ; two morphological features : shape and margin ; blood flow and age . Feed forward Artificial Neural Networks ( ANN ) with structure optimized by SOM/Ward clustering of correlated weighted hidden neuron activation , K-Nearest Neighbour ( KNN ) , Nearest Centroid and Linear Discriminant Analysis ( LDA ) were employed for classification and evaluation . Ninety nine breast sonograms—46 malignant and 53 benign- were evaluated . First , they are sensitive to outliers , noise , and variations in data , which degrades their performances in dimensionality reduction . Second , most of the linear discriminant analysis-based methods only focus on the global structures of data but ignore their local geometric structures , which play important roles in dimensionality reduction . More importantly , the total number of projections obtained by linear discriminant analysis ( LDA ) based methods are limited by the class number in the training data set . Several data mining techniques were explored in this study to investigate their performance in detecting distraction . Among them , the performance of two linear ( linear discriminant analysis and logistic regression ) and two nonlinear models ( support vector machines and random forests ) is reported in this article . Random forests algorithms had the best performance , which detected texting and eating distraction with an accuracy of 85.38 % and 81.26 % , respectively . The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones . This paper reports the results of a linear ( linear discriminant analysis ) and two nonlinear classifiers ( neural networks and support vector machines ) applied to the classification of spontaneous EEG during five mental tasks , showing that nonlinear classifiers produce only slightly better classification results . An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement . A novel hyperspectral imagesfeature extraction method is presented for dimensionality reduction . Firstly , take discrete Fourier transformation ( DFT ) of each pixel spectral curve , andcombine the amplitude spectrum and corresponding phase spectrum ; then directlinear discriminant analysis ( DLDA ) is performed in the combination spectrumspace to extract features . Minimum distance classifier is used to evaluate thefeature extraction performance in the achievedcombination spectrum DLDA subspace . The intrinsic nature of high dimensionality and small sample size in microarray data calls for the development of effective computational methods . In this paper , we propose a novel hybrid dimension reduction technique for classification - hybrid PCA ( principal component analysis ) and LDA ( linear discriminant analysis ) analysis . This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace . A study of generalization error in signal detection by multiple spatially-distributed and -correlated sensors is provided when the detection rule is learned from a finite number of training samples via the classical linear discriminant analysis formulation . Spatial correlation among sensors is modeled by a Gauss-Markov random field defined on a nearest neighbor graph according to inter-sensor spatial distance , where sensors are placed randomly on a growing bounded region of the plane . The probabilities of estimation and the cumulative probability are surely between zero and one . Linear Discriminant Analysis Linear discriminant analysis ( LDA ) is used in statistics to find the linear combination of features which best separate two or more classes of objects or events . The resulting combinations may be used as a linear classifier , or more commonly in dimensionality reduction before later classification . In practice this means that the learned filters will be dominated by background information , and will have minimal information about the pedestrians . Put differently , learning filters from all the data assumes that the decision boundary is defined by a single distribution ( like in Linear Discriminant Analysis ) , while we might want to define it based on the relation between the background distribution and the foreground distribution ( like Fisher ’s Discriminant Analysis ) . In PcaForeground we train filters per feature channel , learned from background image patches , and learned from patches extracted over pedestrians ( see figure [ reference ] ) . Here , a new approach is suggested which is applicable in multi-speaker scenarios also , owing to its incorporation of higher order statistics . Here , independent component analysis is used to obtain estimates of the clean speech and the angles of incidence for each speaker . Subsequently , these estimates can help to correctly identify the active speaker and perform voice activity detection . However , the spectral resolution can be improved by mathematically removing the effect of the instrument response function . An independent component analysis based algorithm is proposed to blindly deconvolve the measured spectroscopic data . The true spectrum and the instrument response function are estimated simultaneously . Therefore , the main purpose of the E-Bra system is to provide a reliable ECG data set identical to the quality of an ECG data set collected in hospital . Unfortunately , removing MA is a big challenge because the frequency range of the MA is duplicated on the frequency range of the pure ECG components , P-QRS-T. In this dissertation , two motion artifact removal algorithms ( MARAs ) with adaptive filter structure and independent component analysis concept are suggested , and the performance of the two MARAs will be evaluated by correlation values and signal noise ratio ( SNR ) values . 1 . INDEPENDENT COMPONENT ANALYSIS Independent component analysis ( ICA ) is an unsuper-vised statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables . Recently , ICA and the closely related blind source separation ( BSS ) problem have attracted a lot interest both in statistical signal processing and neural network communities . While inspired by human perception , such methods did not perform well at the time . Various methods have shown that spatio - temporal filters emerge from learning , for example using independent component analysis , sparseness , and multi - layer models . Memisevic and Hinton learn simple spatial transformations with a restricted Boltzmann machine , finding a variety of filters . Many traditional algorithms over the past few decades like Sauvola , Niblack , Otsu etc , . were used for binarization which gave insufficient results for historical texts with degradations . Recently many attempts have been made to solve binarization using deep learning approaches like Autoencoders , FCNs . However , these models do not generalize well to real world historical document images qualitatively . The second term is based on ranking of individuals , so that R2DE exploits additional information provided by the fitnesses . In experiments including non-linear dimension reduction by autoencoders , it is shown that R2DE improves robustness and speed of global convergence . [ reference ] . The multinomial likelihood is less well studied in the context of latent - factor models such as matrix factorization and autoencoders . A notable exception is the collaborative competitive filtering ( CCF ) model [ reference ] and its successors , which take advantage of more fine - grained information about what options were presented to which users . Vaes on sparse data . Variational autoencoders ( vaes ) [ reference ][ reference ] have seen much application to images since their inception . Doersch [ reference ] presents a review on different applications of vae to image data . One study , amidst the rise of neural network - based approaches to 3D object recognition , that is most relevant to this paper is that of , which presented promising results and a benchmark for 3D model recognition : ModelNet . Following this key study , researchers have tried applying 3D ConvNets , autoencoders , and a variety of probabilistic neural generative models to the problem of 3D model recognition , with each study progressively advancing state - of - the - art . With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods . We formulate our regularization with a k - means style objective which is easy to optimize , and investigate different types of clusterings , including sample clustering , spatial clustering , and co - clustering . We demonstrate the generalization performance of our proposed method in several settings : autoencoders trained on the MNIST dataset [ 23 ] , classification on CIFAR10 and CIFAR100 [ 20 ] , as well as fine - grained classification and zero - shot learning on the CUB - 200 - 2011 dataset [ 34 ] . The MMD objective can be interpreted as minimizing the distance between all moments of the model distribution and the data distribution . It has been shown that GMMNs can be combined with pre - trained dropout autoencoders to achieve better likelihood results ( GMMN + AE ) . Our adversarial autoencoder also relies on the autoencoder to capture the data distribution . section : Conclusion In this paper , we proposed to use the GAN framework as a variational inference algorithm for both discrete and continuous latent variables in probabilistic autoencoders . Our method called the adversarial autoencoder ( AAE ) , is a generative autoencoder that achieves competitive test likelihoods on real - valued MNIST and Toronto Face datasets . Unlike the aforementioned works , we consider an explicitly two - dimensional form of attention , where an array of 2D Gaussian filters is applied to the image , yielding an image ‘ patch ’ of smoothly varying location and zoom . This configuration , which we refer to simply as “ DRAW ” , somewhat resembles the affine transformations used in computer graphics - based autoencoders . As illustrated in Fig . Recent work has explored neural networks for document models [ see e.g. ][] neural_variational_text but has been limited to modelling datapoints with little internal structure . Along related lines are ‘ structured variational autoencoders ’ [ see ][] structured_vae , where they treat the general problem of integrating graphical models with variational autoencoders . paragraph : Transfer learning The transmission medium is modeled as an AWGN channel with additive radar interference . Standard constellations are not optimal in this context , and an auto-encoder ( AE ) is used to design proper constellations and corresponding receiver devices . AE is a powerful tool in neural networks that shares strong similarities with communication systems . This paper presents a summary of an experimental and theoretical study conducted to improve the performance of Acoustic Emission ( AE ) based structural health monitoring . Two broadband AE sensors are attached to a test specimen and a series of artificial test signals were generated using multiple pencil-lead breaks . The geometry of the test specimen is such that ; one of the AE sensors always receives a direct wave and the second sensor receives reflection ( or echo ) of the same direct wave received at the first sensor . Although advanced wireless and sensor fusion technologies have made it possible to develop equipment for such an application , there are some critical issues for such equipment to be effectively and reliably used in infrastructure sites . This paper addresses these critical issues and introduces solutions and state-of-the-art features implemented in a wireless AE system . Laboratory and field tests have been conducted to prove the necessity and effectiveness of the measures taken in the design of wireless AE systems . subsection : Autoencoders The original AE , introduced by Hinton et al . , is a dimensionality reduction technique for high dimensional data such as images , audio or depth . It consists of an Encoder and a Decoder , both arbitrary learnable function approximators which are usually neural networks . The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) . We also include the FastSent variant “ FastSent + AE ” and the SkipThought - LN version that uses layer normalization . We report results from models trained on supervised data in the third group , and also report some results of supervised methods trained directly on each task for comparison with transfer learning approaches .
In multicast networks especially with multiple multicast sources where bottleneck effects may occur frequently , frequently used multicast service leads to inefficient network utilization problems . This paper presents a new network utilization algorithm for multicasting called load distribution algorithm ( LDA ) . The LDA algorithm uses selecting candidate path based on ant colony algorithm and multicast scheduling to distribute the contention multicast packets onto their corresponding candidate paths .
Convergence proof of all algorithms is given using an appropriate cost function and discussing about its initial conditions . Application of the new IFR on feature extraction from facial image sequences is given in two steps : i ) image preprocessing , which includes normalization , histogram equalization , mean centering and background removal , ii ) adaptive LDA feature estimation . In the preprocessing stage , all input images are cropped and prepared for the next step .
In addition to a traditional perplexity measure , I discuss how to use cosine similarity and Symmetric Kullback-Leibler Divergence to evaluate clustering performance . Three examples of using LDA outputs as building blocks for more complicated machine learning system are also demonstrated : 1 ) Cascaded LDA for taxonomy building . 2 ) In-cluster similarity computing .
These transformed signals are decomposed into 8 IMFs . Significant features are extracted from the signals and classified by ANN and LDA method and tabulated . From the results , we found that IMF4 provides the most significant classification accuracies .
subsection : Foley - Sammon Transform The learned null Foley - Sammon transform ( NFST ) space is closely related to linear discriminant analysis ( LDA ) , also known as Foley - Sammon transform ( FST ) . So before we formulate NFST , let us first briefly revisit FST .
Due to the use of a log - log scale , even small improvements in ( log - average ) MR correspond to large reductions in false - positives . On INRIA , our ( slightly modified ) baseline version of ACF scores at 17.3 % MR compared to 17.0 % MR for the model reported in [ 7 ] . 4 Detection with Oblique Splits ( ACF - LDA ) In this section we modify the ACF detector to enable oblique splits and report the resulting gains .
Table 1 , rows 2 - 4 , shows the results of ACF with oblique splits , updated every T boosting iterations 2http: // vision.ucsd.edu / ˜pdollar / toolbox / doc / ( denoted by ACF - LDA - T ) . While more frequent updates improve accuracy , ACF - LDA - 16 has negligibly higher MR than ACF - LDA - 4 but a nearly fourfold reduction in training time ( timed using 12 cores ) . Training the brute force version of ACF - LDA , updated at every iteration and each tree split ( 7 interior nodes per depth - 3 tree ) would have taken about 5 · 4 · 7 = 140 hours . For these results we used regularization of = .1 and patch size of m = 5 ( effect of varying m is explored in § 6 ) .
Most similar to our loss function , Weinberger propose to learn a distance metric for large margin nearest neighbor classification . Kostinger further explore a similar LDA based objective . More recently Schroff , building on Sun , introduced the triplet loss for face recognition .
We increased the number of FBANKs from 24 to 40 , which yielded slightly lower PER . We also evaluated the standard Kaldi features — 39 dimensional MFCCs spliced by a context window of 7 , followed by LDA and MLLT transform and with feature - space speaker - dependent MLLR , which were the same features used in the HMM - DNN baseline in Table [ reference ] . The well - engineered features improved the accuracy of our system by more than 1 % absolute .
SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks , including the object recognition problem . ICA is a feature extraction technique which can be considered a generalization of principal component analysis ( PCA ) . ICA has been mainly used on the problem of blind signal separation .
However , these estimated components oftentimes have higher order dependencies , such as co-activation of components , that are not accounted for in the model . Topographic independent component analysis(TICA ) , a modification of ICA , takes into account higher order dependencies and orders components topographically as a function of dependence . Here , we aim to visualize the time course of TICA basis activations to movie stimuli .
The invention relates to a ICA ( Independent Component Analysis ) shrinkage de-noising method evaluating noise variance evaluation based on a wavelet packet , relating to a structural response signal de-noising and processing technology . The method comprises the following steps of : firstly , evaluating the noise variance by using a wavelet packet ; secondly , carrying out shrinkage estimation by using an ICA shrinkage theory for de-noising ; and finally , reconstructing a signal through ICA inverse transformation . The method can be used for better eliminating the noise in the signal , can also express the superiority in a high-noise environment and can be used for de-noising signals in the fields of civil engineering , aerospace , automatic control , mechanical engineering , bridge engineering , water conservancy projects and the like .
In this paper , we show that minimization of the statistical dependence using broadband independent component analysis ( ICA ) can be successfully exploited for acoustic source localization . As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths , the ICA criterion offers a theoretically more rigorous framework than conventional techniques based on an idealized single-path and single-source signal model . This leads to algorithms which outperform other localization methods , especially in the presence of multiple simultaneously active sound sources and under adverse conditions , notably in reverberant environments .
The simulation is performed by using the ORL database , where the face images vary in illumination , expression , pose , and scale . The results show that the PICF method using the corresponding similarity measurement has better retrieving performance than classical PCA or ICA method with the usual measurement , and the two-stage-accuracy can reach 97.5 %
Training such a generative network that maps latent variable to a sample does not in theory require a discriminator network as in GANs , or approximate inference as in variational autoencoders . Indeed , if is bijective , it can be trained through maximum likelihood using the change of variable formula : This formula has been discussed in several papers including the maximum likelihood formulation of independent components analysis ( ICA ) bell1995information , hyvarinen2004independent , gaussianization NIPS1994_901 , chen2000gaussianization and deep density models bengio1991artificial , rippel2013high , dinh2014nice , balle2015density . As the existence proof of nonlinear ICA solutions hyvarinen1999nonlinear suggests , auto - regressive models can be seen as tractable instance of maximum likelihood nonlinear ICA , where the residual corresponds to the independent components . However , naive application of the change of variable formula produces models which are computationally expensive and poorly conditioned , and so large scale models of this type have not entered general use .
Nevertheless , little effort has been devoted to address this problem ( see Table [ reference ] for a comparison of meta - parameters required by unsupervised feature learning methods ) . To the best of our knowledge , work in this direction includes ICA and sparse filtering . Although ICA provides good results at object recognition tasks , the method scales poorly to large datasets and high input dimensionality .
Commonly used algorithms for unsupervised feature learning include Restricted Boltzmann Machines ( RBM ) , auto - encoders , sparse coding and hybrids such as PSD . Many other methods such as ICA , Reconstruction ICA ( RICA ) , Sparse Filtering and methods related to vector quantization such as Orthogonal Matching Pursuit ( OMP - k ) have also been used in the literature to extract unsupervised feature representations . These algorithms could be divided into two categories : explicitly modeling or not the input distribution .
Kipf and Welling report their mean GCN and ICA results on the same data splits over 100 runs with random weight initializations . The other baseline methods are taken from Yang et al . .
First , a comprehensive evaluation index system of logistics service providers selection is put forward in the paper . Then , In order to overcome the lack of previous evaluation methods , projection pursuit method based on genetic algorithm for the uncertain and high-dimensional nature of the evaluation indicator data of logistics service suppliers is suggest whose essence is a dimensionality reduction technology in order to meet the purpose of analyzing high-dimensional and non-linear data in the low-dimensional space . The model can determine the index objective weights result directly from sample data of candidate schemes themselves , and can avoid the subjective negative impact on conclusion in subjective evaluation methods .
Without supervised information , current deep learning methods are difficult to be directly applied to image clustering problems . Image clustering needs to deal with three main problems : 1 ) the curse of dimensionality caused by high-dimensional image data ; 2 ) extracting the effective image features ; 3 ) combining feature extraction , dimensionality reduction and clustering . In this paper , we propose a new clustering framework called Deep Embedded Dimensionality Reduction Clustering ( DERC ) via Probability-Based Triplet Loss , which effectively solves the above issues .
Multiple correspondence analysis is a dimension reduction technique which plays a large role in the analysis of tables with categorical nominal variables , such as survey data . Though it is usually motivated and derived using geometric considerations , we prove that in fact , it can be seen as a single proximal Newton step of a natural bilinear exponential family model for categorical data : the multinomial logit bilinear model .
The central idea of the proposed representation takes root in the Whitney embedding theorem which guarantees an embedding of a one dimensional manifold in as a space curve . The resulting of such dimension reduction , is a simplification of comparing two behaviors to that of comparing two curves in R3 . This comparison is additionally theoretically and numerically easier to implement for statistical analysis .
In this work , we propose a new idea which we named Multi-class Fukunaga Koontz Discriminant Analysis ( FKDA ) by incorporating the Fukunaga Koontz Transform within the optimization for maximizing class separation criteria in LDA , UDP , and LPP . In contrast to traditional LDA , UDP , and LPP , our approach can work with very high dimensional data as input , without requiring a separate dimensionality reduction step to make the scatter matrices full rank . In addition , the FKDA formulation seeks optimal projection direction vectors that are orthogonal which the existing methods can not guarantee , and it has the capability of finding the exact solutions to the " trace ratio " objective in discriminant analysis problems while traditional methods can only deal with a relaxed and inexact " ratio trace " objective .
Selections of records can be made and examined , and dependencies between fields can be investigated in detail . MADEN was used as a tool for investigating and assessing many data processing algorithms , firstly data-reducing ( clustering ) methods , then dimensionality-reducing techniques . These included a new ' directed ' form of principal components analysis , several novel applications of artificial neural networks , and discriminant analysis techniques which illustrated how groups within a database can be separated .
We introduce GEPIA ( Gene Expression Profiling Interactive Analysis ) , a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data . GEPIA provides key interactive and customizable functions including differential expression analysis , profiling plotting , correlation analysis , patient survival analysis , similar gene detection and dimensionality reduction analysis . The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas , scientific discussion and the therapeutic discovery process .
According to the features of human finger vein image , a finger vein feature extraction method based on dimensionality reduction idea is put forward . In this method , the vein feature extraction is carried out through searching for valley shape region that is formed by the vein of two dimension gray image on one dimension gray curve .
However , processing this kind of streaming data on-line is a hard problem . This paper address the use of a dimensionality reduction perspective to classify streaming data and present a preliminary work for its adaptation to new data in changeable environments . This new extended proposal allows from a computational point of view the treatment of the new data without any penalty and evolving the previously analyzed data .
Unsupervised learning methods offer one promising avenue for acquiring a concise representation of the state with a good similarity metric . This can be accomplished using dimensionality reduction , clustering , or graph - based techniques . In our work , we draw on recent developments in representation learning with neural networks , as discussed in the following section .
However , current techniques fail to satisfactorily define and optimize a reasonable objective required for scalable unsupervised feature learning in networks . Classic approaches based on linear and non - linear dimensionality reduction techniques such as Principal Component Analysis , Multi - Dimensional Scaling and their extensions optimize an objective that transforms a representative data matrix of the network such that it maximizes the variance of the data representation . Consequently , these approaches invariably involve eigendecomposition of the appropriate data matrix which is expensive for large real - world networks .
Unsupervised feature learning approaches typically exploit the spectral properties of various matrix representations of graphs , especially the Laplacian and the adjacency matrices . Under this linear algebra perspective , these methods can be viewed as dimensionality reduction techniques . Several linear ( e.g. , PCA ) and non - linear ( e.g. , IsoMap ) dimensionality reduction techniques have been proposed .
3DMM has been widely applied in numerous areas , such as computer vision , graphics , human behavioral analysis and craniofacial surgery . 3DMM is learnt through supervision by performing dimension reduction , normally Principal Component Analysis ( PCA ) , on a training set of face images / scans . To model highly variable 3D face shapes , a large amount of high - quality 3D face scans is required .
This might prove to be helpful in systems for detecting relatively small objects . We have studied how factorizing convolutions and aggressive dimension reductions inside neural network can result in networks with relatively low computational cost while maintaining high quality . The combination of lower parameter count and additional regularization with batch - normalized auxiliary classifiers and label - smoothing allows for training high quality networks on relatively modest sized training sets .
However , the computational complexity of 3D search typically grows cubically with respect to resolution and becomes too expensive for large scenes or real - time applications such as autonomous driving . Instead , in this work , we reduce the search space following the dimension reduction principle : we take the advantage of mature 2D object detectors ( Fig . [ reference ] ) .
Covariance pooling was initially used in for pooling covariance matrix from the outputs of CNNs . proposed an alternative to compute second - order statistics in the setting of CNNs . However , such two works do not use either dimensionality reduction layers or non - linear rectification layers for second - order statistics . In this paper , we present a strong motivation for exploring them in the context of facial expression recognition .
Finally , the integrated memory is passed into a bi - directional LSTM , and the output will captures the interaction among the context words and the query words : In multiple layers , the integrated hierarchical matching module can be regarded as the input of next layer after a dimensionality reduction processing . We call this memory networks of full - orientation matching .
[ reference ] is the FCN ( also FCN - Basic ) decoding technique . The important design element of the FCN model is dimensionality reduction step of the encoder feature maps . This compresses the encoder feature maps which are then used in the corresponding decoders .
DM-based CAD ( CADDM ) and DBT-based CAD ( CADDBT ) were previously developed by our group . The major differences between the CAD systems include : ( a ) CADDBT uses two parallel processes whereas CADDM uses a single process for enhancing MCs and removing the structured background , ( b ) CADDBT has additional processing steps to reduce the false positives ( FPs ) , including ranking of candidates of cluster seeds and cluster members and the use of adaptive CNR and size thresholds at clustering and FP reduction , ( c ) CADDM uses convolution neural network ( CNN ) and linear discriminant analysis ( LDA ) to differentiate true microcalcifications from FPs based on their morphological and CNN features . The performance difference is assessed by FROC analysis using test set ( 100 views with MCs and 74 views without MCs ) independent of their respective training sets .
To demonstrate the effect of CRD on differentiating malignant from benign masses and the potential improvement to the diagnostic accuracy of breast cancer using ultrasound , this chapter evaluates the diagnostic accuracy of different classifiers when the CRD was added to five powerful mass features obtained from previous studies including one geometric feature : Depth-Width ratio ( DW ) ; two morphological features : shape and margin ; blood flow and age . Feed forward Artificial Neural Networks ( ANN ) with structure optimized by SOM/Ward clustering of correlated weighted hidden neuron activation , K-Nearest Neighbour ( KNN ) , Nearest Centroid and Linear Discriminant Analysis ( LDA ) were employed for classification and evaluation . Ninety nine breast sonograms—46 malignant and 53 benign- were evaluated .
First , they are sensitive to outliers , noise , and variations in data , which degrades their performances in dimensionality reduction . Second , most of the linear discriminant analysis-based methods only focus on the global structures of data but ignore their local geometric structures , which play important roles in dimensionality reduction . More importantly , the total number of projections obtained by linear discriminant analysis ( LDA ) based methods are limited by the class number in the training data set .
Several data mining techniques were explored in this study to investigate their performance in detecting distraction . Among them , the performance of two linear ( linear discriminant analysis and logistic regression ) and two nonlinear models ( support vector machines and random forests ) is reported in this article . Random forests algorithms had the best performance , which detected texting and eating distraction with an accuracy of 85.38 % and 81.26 % , respectively .
The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones . This paper reports the results of a linear ( linear discriminant analysis ) and two nonlinear classifiers ( neural networks and support vector machines ) applied to the classification of spontaneous EEG during five mental tasks , showing that nonlinear classifiers produce only slightly better classification results . An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement .
A novel hyperspectral imagesfeature extraction method is presented for dimensionality reduction . Firstly , take discrete Fourier transformation ( DFT ) of each pixel spectral curve , andcombine the amplitude spectrum and corresponding phase spectrum ; then directlinear discriminant analysis ( DLDA ) is performed in the combination spectrumspace to extract features . Minimum distance classifier is used to evaluate thefeature extraction performance in the achievedcombination spectrum DLDA subspace .
The intrinsic nature of high dimensionality and small sample size in microarray data calls for the development of effective computational methods . In this paper , we propose a novel hybrid dimension reduction technique for classification - hybrid PCA ( principal component analysis ) and LDA ( linear discriminant analysis ) analysis . This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace .
A study of generalization error in signal detection by multiple spatially-distributed and -correlated sensors is provided when the detection rule is learned from a finite number of training samples via the classical linear discriminant analysis formulation . Spatial correlation among sensors is modeled by a Gauss-Markov random field defined on a nearest neighbor graph according to inter-sensor spatial distance , where sensors are placed randomly on a growing bounded region of the plane .
The probabilities of estimation and the cumulative probability are surely between zero and one . Linear Discriminant Analysis Linear discriminant analysis ( LDA ) is used in statistics to find the linear combination of features which best separate two or more classes of objects or events . The resulting combinations may be used as a linear classifier , or more commonly in dimensionality reduction before later classification .
In practice this means that the learned filters will be dominated by background information , and will have minimal information about the pedestrians . Put differently , learning filters from all the data assumes that the decision boundary is defined by a single distribution ( like in Linear Discriminant Analysis ) , while we might want to define it based on the relation between the background distribution and the foreground distribution ( like Fisher ’s Discriminant Analysis ) . In PcaForeground we train filters per feature channel , learned from background image patches , and learned from patches extracted over pedestrians ( see figure [ reference ] ) .
Here , a new approach is suggested which is applicable in multi-speaker scenarios also , owing to its incorporation of higher order statistics . Here , independent component analysis is used to obtain estimates of the clean speech and the angles of incidence for each speaker . Subsequently , these estimates can help to correctly identify the active speaker and perform voice activity detection .
However , the spectral resolution can be improved by mathematically removing the effect of the instrument response function . An independent component analysis based algorithm is proposed to blindly deconvolve the measured spectroscopic data . The true spectrum and the instrument response function are estimated simultaneously .
Therefore , the main purpose of the E-Bra system is to provide a reliable ECG data set identical to the quality of an ECG data set collected in hospital . Unfortunately , removing MA is a big challenge because the frequency range of the MA is duplicated on the frequency range of the pure ECG components , P-QRS-T. In this dissertation , two motion artifact removal algorithms ( MARAs ) with adaptive filter structure and independent component analysis concept are suggested , and the performance of the two MARAs will be evaluated by correlation values and signal noise ratio ( SNR ) values .
1 . INDEPENDENT COMPONENT ANALYSIS Independent component analysis ( ICA ) is an unsuper-vised statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables . Recently , ICA and the closely related blind source separation ( BSS ) problem have attracted a lot interest both in statistical signal processing and neural network communities .
While inspired by human perception , such methods did not perform well at the time . Various methods have shown that spatio - temporal filters emerge from learning , for example using independent component analysis , sparseness , and multi - layer models . Memisevic and Hinton learn simple spatial transformations with a restricted Boltzmann machine , finding a variety of filters .
Many traditional algorithms over the past few decades like Sauvola , Niblack , Otsu etc , . were used for binarization which gave insufficient results for historical texts with degradations . Recently many attempts have been made to solve binarization using deep learning approaches like Autoencoders , FCNs . However , these models do not generalize well to real world historical document images qualitatively .
The second term is based on ranking of individuals , so that R2DE exploits additional information provided by the fitnesses . In experiments including non-linear dimension reduction by autoencoders , it is shown that R2DE improves robustness and speed of global convergence .
[ reference ] . The multinomial likelihood is less well studied in the context of latent - factor models such as matrix factorization and autoencoders . A notable exception is the collaborative competitive filtering ( CCF ) model [ reference ] and its successors , which take advantage of more fine - grained information about what options were presented to which users .
Vaes on sparse data . Variational autoencoders ( vaes ) [ reference ][ reference ] have seen much application to images since their inception . Doersch [ reference ] presents a review on different applications of vae to image data .
One study , amidst the rise of neural network - based approaches to 3D object recognition , that is most relevant to this paper is that of , which presented promising results and a benchmark for 3D model recognition : ModelNet . Following this key study , researchers have tried applying 3D ConvNets , autoencoders , and a variety of probabilistic neural generative models to the problem of 3D model recognition , with each study progressively advancing state - of - the - art . With respect to 3D object generation from 2D images , commonly used methods can be roughly grouped into two categories : 3D voxel prediction and mesh - based methods .
We formulate our regularization with a k - means style objective which is easy to optimize , and investigate different types of clusterings , including sample clustering , spatial clustering , and co - clustering . We demonstrate the generalization performance of our proposed method in several settings : autoencoders trained on the MNIST dataset [ 23 ] , classification on CIFAR10 and CIFAR100 [ 20 ] , as well as fine - grained classification and zero - shot learning on the CUB - 200 - 2011 dataset [ 34 ] .
The MMD objective can be interpreted as minimizing the distance between all moments of the model distribution and the data distribution . It has been shown that GMMNs can be combined with pre - trained dropout autoencoders to achieve better likelihood results ( GMMN + AE ) . Our adversarial autoencoder also relies on the autoencoder to capture the data distribution .
section : Conclusion In this paper , we proposed to use the GAN framework as a variational inference algorithm for both discrete and continuous latent variables in probabilistic autoencoders . Our method called the adversarial autoencoder ( AAE ) , is a generative autoencoder that achieves competitive test likelihoods on real - valued MNIST and Toronto Face datasets .
Unlike the aforementioned works , we consider an explicitly two - dimensional form of attention , where an array of 2D Gaussian filters is applied to the image , yielding an image ‘ patch ’ of smoothly varying location and zoom . This configuration , which we refer to simply as “ DRAW ” , somewhat resembles the affine transformations used in computer graphics - based autoencoders . As illustrated in Fig .
Recent work has explored neural networks for document models [ see e.g. ][] neural_variational_text but has been limited to modelling datapoints with little internal structure . Along related lines are ‘ structured variational autoencoders ’ [ see ][] structured_vae , where they treat the general problem of integrating graphical models with variational autoencoders . paragraph : Transfer learning
The transmission medium is modeled as an AWGN channel with additive radar interference . Standard constellations are not optimal in this context , and an auto-encoder ( AE ) is used to design proper constellations and corresponding receiver devices . AE is a powerful tool in neural networks that shares strong similarities with communication systems .
This paper presents a summary of an experimental and theoretical study conducted to improve the performance of Acoustic Emission ( AE ) based structural health monitoring . Two broadband AE sensors are attached to a test specimen and a series of artificial test signals were generated using multiple pencil-lead breaks . The geometry of the test specimen is such that ; one of the AE sensors always receives a direct wave and the second sensor receives reflection ( or echo ) of the same direct wave received at the first sensor .
Although advanced wireless and sensor fusion technologies have made it possible to develop equipment for such an application , there are some critical issues for such equipment to be effectively and reliably used in infrastructure sites . This paper addresses these critical issues and introduces solutions and state-of-the-art features implemented in a wireless AE system . Laboratory and field tests have been conducted to prove the necessity and effectiveness of the measures taken in the design of wireless AE systems .
subsection : Autoencoders The original AE , introduced by Hinton et al . , is a dimensionality reduction technique for high dimensional data such as images , audio or depth . It consists of an Encoder and a Decoder , both arbitrary learnable function approximators which are usually neural networks .
The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) . We also include the FastSent variant “ FastSent + AE ” and the SkipThought - LN version that uses layer normalization . We report results from models trained on supervised data in the third group , and also report some results of supervised methods trained directly on each task for comparison with transfer learning approaches .
Ant Colony-Based Load Balancing Algorithm for Multi-Source Multicast Networks A Face Recognition System using Neural Networks with Incremental Learning Ability Application of Latent Dirichlet Allocation in Online Content Generation - eScholarship Towards the Effective Intrinsic Mode Functions for Motor Imagery EEG Signal Classification Learning a Discriminative Null Space for Person Re-identification Local Decorrelation For Improved Pedestrian Detection Semantic Instance Segmentation with a Discriminative Loss Function Segmental Recurrent Neural Networks for End-to-End Speech Recognition Face recognition using independent component analysis and support vector machines Visualizing Topographic Independent Component Analysis with Movies ICA (Independent Component Analysis) shrinkage de-noising method evaluating noise variance based on wavelet packet TDOA Estimation for Multiple Sound Sources in Noisy and Reverberant Environments Using Broadband Independent Component Analysis Locally Salient Feature Extraction Using ICA for Content-Based Face Image Retrieval Density estimation using Real NVP No more meta-parameter tuning in unsupervised sparse feature learning Learning to Make Predictions on Graphs with Autoencoders Research on the Selection of Logistics Service Suppliers Based on Projection Pursuit Image Clustering via Deep Embedded Dimensionality Reduction and Probability-Based Triplet Loss Multiple correspondence analysis and the multilogit bilinear model A curve representation of human activity Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition Exploratory database visualisation : the application and assessment of data and dimensionality reduction GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses Study on the Finger Vein Feature Extraction Algorithm User modeling in changeable environments Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models node2vec: Scalable Feature Learning for Networks Nonlinear 3D Face Morphable Model Rethinking the Inception Architecture for Computer Vision Frustum PointNets for 3D Object Detection from RGB-D Data Covariance Pooling for Facial Expression Recognition MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Comparison of computer-aided detection of clustered microcalcifications in digital mammography and digital breast tomosynthesis Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Incorporating a New Feature of Mass Central Regularity Degree (CRD) Robust Locally Discriminant Analysis via Capped Norm Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics Comparison of linear, nonlinear, and feature selection methods for EEG signal classification Hyperspectral Images Terrain Classification in Combination Spectrum DLDA Subspace Hybrid PCA and LDA Analysis of Microarray Gene Expression Data Spatially-correlated sensor discriminant analysis Applying Data Classification Techniques for Churn Prediction in Retailing Filtered channel features for pedestrian detection Multi-speaker voice activity detection using ICA and beampattern analysis Independent Component Analysis Based Blind Deconvolution of Spectroscopic Data System Design And Motion Artifact Removal Algorithm Implementation For Ambulatory Women Ecg Measurement System:e-Bra System A Complexity Minimization Approach for Estimating Fading Gaussian Channel in CDMA Communications Optical Flow Estimation Using a Spatial Pyramid Network Removal of Historical Document Degradations using Conditional GANs Randomized and Rank Based Differential Evolution Variational Autoencoders for Collaborative Filtering Learning a Hierarchical Latent-Variable Model of 3D Shapes Learning Deep Parsimonious Representations Adversarial Autoencoders DRAW: A Recurrent Neural Network For Image Generation Towards a Neural Statistician Deep Learning Constellation Design for the AWGN Channel With Additive Radar Interference Experimental and Theoretical Study on the Robustification of Acoustic Emission Inspection with Recurrent Neural Networks State-of-the-Art Wireless Acoustic Emission System for Structural Health Monitoring Implicit 3D Orientation Learning for 6D Object Detection from RGB Images Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
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CIRA 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ArXiv INTERSPEECH ICICIC (1) ICLR ArXiv 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) EAIS ArXiv KDD 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence Medical Imaging: Computer-Aided Diagnosis CIBCB ICASSP 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EUSIPCO ICNC (1) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICPRAM ICMLA WWW 2018 International Conference on 3D Vision (3DV) NIPS ArXiv ICML ICLR ECCV EMNLP
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