Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization . Community detection based on overlapping nodes has become one of the most popular methods in recent years . We consider the community detection problem in sparse random hypergraphs . Angelini et al. ( 2015 ) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model ( HSBM ) . An efficient way to detect a community corresponding to a specific topic can greatly help users to obtain useful information . However , existing community detecting algorithms are all based on pages as units , the topic drift phenomenon becomes the inherent problem of each existing community detecting algorithm since the web pages always have multiple topics in content . This paper puts forward a new community detecting algorithm based on blocks as units , which combines the page dividing algorithm with the primary community detecting algorithm , overcoming the defect in the existing community detecting algorithms . MethodsThe study draws on a unique dataset of transaction level data from an encrypted online drug market . Structural measures and community detection analysis are used to characterize and investigate the network structure . Exponential random graph modeling is used to evaluate which vendor characteristics explain variation in purchasing patterns . Varying the Role of Attributes in Community Detection . Much of the community detection literature studies structural communities , communities defined solely by the connectivity patterns of the network . Often networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network . A community detection algorithm is then proposed to recover community structure and to estimate communication probability parameters . It is verified that the community detection part converges in finite time , and the parameter estimation part converges almost surely . Simulations are given to illustrate algorithm performance . While most works approach it as a simple categorization problem , sentiment analysis is actually a suitcase research problem that requires tackling many NLP tasks , including word polarity disambiguation , subjectivity detection , personality recognition , microtext normalization , concept extraction , time tagging , and aspect extraction . Sentiment analysis has raised growing interest both within the scientific community , leading to many exciting open challenges , as well as in the business world , due to the remarkable benefits to be had from financial and political forecasting , e - health and e - tourism , user profiling and community detection , manufacturing and supply chain applications , human communication comprehension and dialogue systems , etc . In the field of emotion recognition , early works by and showed that fusion of audio and visual systems , creating a bimodal signal , yielded a higher accuracy than any unimodal system . Hierarchical clustering requires minimal parameters , has flexibility with similarity measure , and has strong visualization power , all of which makes it ideal for exploratory analysis . Hierarchical clustering is a particular branch of clustering algorithms where the results are not given as partitions , but rather a nested structure , which can represent the ordering among elements within a dataset . The study of the performance of hierarchical structure on time series data is limited . We described our approach to the author clustering task at PAN 2017 , which resulted in the best-performing system at the aforementioned task . Our method performs a hierarchical clustering analysis using document features such as typed and untyped character n-grams , word n-grams , and stylometric features . We experimented with two feature representation methods , log-entropy model , and TF-IDF , while tuning minimum frequency threshold values to reduce the feature dimensionality . Therefore , colorimetric information can improve the process . In this paper , we propose a hierarchical clustering based approach to extract dominant color masks of documents . Indeed , our dataset comprises different kind of scanned administrative document images such as invoices , forms , letters , and so on . The second distinction lies in an efficient algorithm for obtaining a desirable profile , via exploring and exploiting multi-scale cluster structure hidden in but intrinsic to the data . The algorithm accomplishes its task with key components for lower-dimensional embedding with data-specific principal feature axes , hierarchical data clustering , multi-level matrix compression storage , and multi-level interaction computations . We provide experimental results from case studies with two important data analysis algorithms . In Data clustering ( a sub field of Data mining ) , k-means and hierarchical based clustering algorithms are popular due to its excellent performance in clustering of large data sets . This paper presents two different comparative studies which includes various Data Clustering algorithms for analyzing best one with minimum clustering error . One of the challenges in sensor networks , routing and transmission of data in networks , which is of high importance , So having a good routing algorithm can help to improve network performance . In this paper , a new method based on a hierarchical structure ( clustering ) is introduced in terms of three factors that are considered in the route selection discussion : Interference , energy consumption and deadline . In routing , when a packet is to be sent , the cluster heads depending on the stipulated time sends it . In order to exploit the assumption of a hierarchical generative process over datasets we will use a ‘ parameter - transfer approach ’ [ see ][] transfer_survey to extend the variational autoencoder model of variational_autoencoder . figure Left : basic hierarchical model , where the plate encodes the fact that the context variable is shared across each item in a given dataset . Center : full neural statistician model with three latent layers . A very natural and intuitive alternative organizes classifiers in a hierarchical manner according to the divide - and - conquer strategy . Although hierarchical classification has been proven effective for conventional linear classifiers , few attempts have been made to exploit category hierarchies in deep CNN models . Since deep CNN models are large models themselves , organizing them hierarchically imposes the following challenges . In summary , this paper has the following contributions . First , we introduce a novel hierarchical architecture , called HD - CNN , for image classification . Second , we develop a scheme for learning the two - level organization of coarse and fine categories , and demonstrate various components of an HD - CNN can be independently pretrained . The corresponding question - level feature is the LSTM hidden vector at time . Our hierarchical representation of the question is depicted in Fig . [ reference ] ( a ) . [ reference ][ reference ] . Unfortunately , this kind of approach is not well suited to the case where different segments in the hierarchical decomposition have different lengths : for example , different words have different lengths , so a fixed hierarchy would not update its upper - level units in synchrony with the natural boundaries in the data . section : THE PROPOSED MODEL We parametrize our convolution kernels in a similar flavor but in the spectral domain , which turns out to be straightforward and effective . Essentially , we find that multi - resolution analysis on graphs could be achieved without complicated hierarchical graph clustering . Before explaining what the exact parametrization is , we first discuss the intuition behind our design . Gupta et al . use boundary detection and hierarchical grouping before performing category segmentation . The common attribute in all these approaches is the use of hand engineered features for classification of either RGB or RGBD images . Spans exhaustive marking of text spans containing information relevant to the respective PIO categories ( Stage 1 annotation ) . Hierarchical labels assignment of more specific labels to subsequences comprising the marked relevant spans ( Stage 2 annotation ) . Repetition grouping of labeled tokens to indicate repeated occurrences of the same information ( Stage 2 annotation ) . Given image and class embeddings , we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones ; zero - shot classification of an image proceeds by finding the label yielding the highest joint compatibility score . We use state - of - the - art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora . We establish a substantially improved state - of - the - art on the Animals with Attributes and Caltech - UCSD Birds datasets . Its applicability is illustrated by comparing , on the one hand , some reduction-to-pole algorithms and on the other , the ( well-known ) k-means algorithm . Finally , the paper shows the results obtained by applying the Mean Shift algorithm as an alternative method to ‘ unsupervised clustering ’ of anomalies that appear in images obtained from geophysical data , in which the a priori knowledge of the number of classes is difficult or impossible . Detecting communities has long been popular in the research on networks . It is usually modeled as an unsupervised clustering problem on graphs , based on heuristic assumptions about community characteristics , such as edge density and node homogeneity . In this work , we doubt the universality of these widely adopted assumptions and compare human labeled communities with machine predicted ones obtained via various mainstream algorithms . Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets . We introduce unsupervised learning ( clustering ) where optimization is supported by the linguistic granules of context , thereby giving rise to so-called context-sensitive fuzzy clustering . The combination of neuro , evolutionary , and granular computing in the context of data mining is explored . This paper presents a novel hardware Trojan detection technique in gate-level netlist based on the controllability and observability analyses . Using an unsupervised clustering analysis , the paper shows that the controllability and observability characteristics of Trojan gates present significant inter-cluster distance from those of genuine gates in a Trojan-inserted circuit , such that Trojan gates are easily distinguishable . The proposed technique does not require any golden model and can be easily integrated into the current integrated circuit design flow . This paper presents an approach for improving performances of the unsupervised classification ( clustering ) in remote sensing imagery by proposing a technique to combine the classical techniques of K-means clustering and the Gaussian mixture model ( GMM ) with expectation-maximization ( EM ) . The proposed model means to apply firstly K-means clustering , and to use the result of this technique to compute means μk , covariance matrices Σk and mixing coefficients πk , considered as initialization parameters for the next stage of GMM-EM . In this article , an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented . Rain blurs the wave signatures of radar images , and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures . Relying on this , the representation learning and clustering for short text are seamlessly integrated into a unified framework . To further facilitate the model training process , we apply adversarial training to the unsupervised clustering setting , by adding perturbations to the cluster representations . The model parameters and perturbations are optimized alternately through a minimax game . The Gamma Test ( GT ) is used to help facilitate the best use of limited data and to ensure that the ANNs are not over trained . The palynomorph database created with the greatest amount of salient features is then used with an unsupervised ANN clustering algorithm to predict groups within the dinoflagellate cysts . The results demonstrate that the system developed is able to achieve an average correct classification rate of > 90 % . One such extension is to make the prior mean depend on higher-level task characteristics . More unsupervised clustering of tasks is obtained if we go from a single Gaussian prior to a mixture of Gaussians . This can be further generalized to a mixture of experts architecture with the gates depending on task characteristics . In this context , present a generative model for image synthesis that yields a probabilistic segmentation of abnormalities . Another unsupervised technique is proposed by , a saliency - based method that exploits brain asymmetry in pathological cases . A common advantage of the above methods is that they do not require a training dataset with corresponding manual annotations . Backpropagation through this fixed model modulates the loss on the learned embedding , increasing the gradient for initial embedding vectors whose instance membership is ambiguous and decreasing the gradient for embedding vectors that will be correctly resolved by the recurrent grouping phase . Figure [ reference ] shows a toy example highlighting the difference between supervised and unsupervised clustering . We generate a set of 1 - D data points drawn from three Gaussian distributions with mean and standard deviation as , and , respectively , as shown in Figure [ reference ] We analyze the behavior of Gaussian Blurring Mean Shift ( GBMS ) with bandwidth as . If running GBMS for unsupervised clustering on these data with the default setting ( bandwidth is 0.2 ) , we can see they are grouped into three piles , as shown in Figure [ reference ] ( b ) . For example , “ powerful , ” “ strong ” and “ Paris ” are equally distant . In this paper , we propose Paragraph Vector , an unsupervised algorithm that learns fixed - length feature representations from variable - length pieces of texts , such as sentences , paragraphs , and documents . Our algorithm represents each document by a dense vector which is trained to predict words in the document . In this section , we discuss how the PixelGAN autoencoder can be viewed in the context of learning cross - domain relations between two different domains . We also describe how the problem of clustering or semi - supervised learning can be cast as the problem of finding a smooth cross - domain mapping from the data distribution to the categorical distribution . Recently several GAN - based methods have been developed to learn a cross - domain mapping between two different domains discogan , cyclegan , cross - domain - ilya , aae , cross - domain - nlp . document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose . Alignment methods exist on a supervision spectrum from requiring manually labeled fiducial points or landmarks , to requiring class labels , to fully unsupervised joint alignment and clustering models . Congealing is an unsupervised joint alignment method based on an entropy objective . We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes . However , their main drawback is their slowness . In this paper , a simple agglomerative clustering algorithm with a low computational complexity , is proposed . This method is especially convenient for performing clustering on large data sets , and could also be used as a linear time initialization method for other clustering algorithms , like the commonly used k-means algorithm . Joint learning may allow for enhanced attribute classification when there is inherent dependency among the attributes . We propose an agglomerative clustering scheme to determine which sets of attributes should be learned jointly in order to maximize the margin of performance improvement . We evaluate the joint learning algorithm on a set of attributes for the task of person re-identification . Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code . The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image , and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes . The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image . A ground-truth strategy is employed to evaluate the accuracy of segmentation . The agglomerative based clustering and their ensemble version achieved the best clustering results . However they suffer from high run-time . We study an agglomerative clustering problem motivated by visualizing disjoint glyphs ( represented by geometric shapes ) centered at specific locations on a geographic map . As we zoom out , the glyphs grow and start to overlap . Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion . Agglomerative clustering and partially ordered sets of HOG features have been used to group images , and exemplars define a group as a set of random transformations applied to a single image . Note the latter does not scale easily , in particular to image segmentation where a single image would call for 40k classes . The fraction of pairs of samples within a class among all possible pairs which are placed in the same cluster , over the total number of same - class pairs . Using these metrics , the F - score is computed as : The simplest way we found to demonstrate the effectiveness of our deep features and the proposed TPE method , is to use the standard MATLAB implementation of the agglomerative clustering algorithm with the average linkage metric . We use the cosine similarity as our basic clustering metric . In many applications of document clustering , a document may include multiple topics and thus may relate to multiple categories at the same time . Most of the existing subspace clustering algorithms can only perform hard clustering on document collections . In this paper , a fuzzy algorithm named R-FPC is introduced for document clustering . Unsupervised learning algorithms manage only unlabeled data . 1.1 The clustering problem Clustering is the most important unsupervised learning problem , and cluster analysis is used in a variety of scientific disciplines such as biology , psychology , medicine , marketing , computer vision and many others . Organizing data into coherent groupings is indeed one of the most fundamental modes of understanding and learning . Land use zoning is essentially a classification problem , so how to apply the idea of fuzzy classification to discuss the optimal regionalization number strategy of land use shall be valuable for providing decision assistance for regional land use optimal zoning . But current research on land use zoning is usually conducted by adoption of traditional hard classification clustering method , of which the classification number is of a strong subjectivity , ignoring the existence of the objectivity of optimal zoning number . This study proposed a quantitative calculation method for land use optimal regionalization number by applying factor analysis , c-means and fuzzy c-means algorithm complying with the efficient maximum membership principle . document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . The model discovers clusters that accurately match semantic classes , achieving state - of - the - art results in eight unsupervised clustering benchmarks spanning image classification and segmentation . At the same time , these clusters are exposed to an increasing number of attacks coming from public networks . Therefore , mechanisms for efficiently and flexibly managing security have now become an essential requirement for clusters . However , despite the growing importance of cluster security , this field has been only minimally addressed by contemporary cluster administration techniques . In this paper , we propose a novel deep network for WSOD . Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning ( MIL ) , our strategy generates proposal clusters to learn refined instance classifiers by an iterative process . The proposals in the same cluster are spatially adjacent and associated with the same object . We have proposal clusters according to ( for background and others for objects ) . For object clusters , the - th cluster consists of proposal boxes , an object label that is the same as the cluster center label , and a confidence score that is the same as the cluster center score , where indicates the confidence that corresponds to an object of class . Unlike object clusters , the background cluster consists of proposals and a label indicating the background . In practice , however , there are significant algorithmic and performance challenges . In this work , we address these challenges and finally realize the promise of conditional computation , achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters . We introduce a Sparsely - Gated Mixture - of - Experts layer ( MoE ) , consisting of up to thousands of feed - forward sub - networks . We stick to the simplest parsers in each category – greedy inference for the transition - based architecture , and a first - order , arc - factored model for the graph - based architecture . Despite the simplicity of the parsing architectures and the feature functions , we achieve near state - of - the - art parsing accuracies in both English ( 93.1 UAS ) and Chinese ( 86.6 UAS ) , using a first - order parser with two features and while training solely on Treebank data , without relying on semi - supervised signals such as pre - trained word embeddings , word - clusters , or techniques such as tri - training . When also including pre - trained word embeddings , we obtain further improvements , with accuracies of 93.9 UAS ( English ) and 87.6 UAS ( Chinese ) for a greedy transition - based parser with 11 features , and 93.6 UAS ( En ) / 87.4 ( Ch ) for a greedy transition - based parser with 4 features . The compact D - dimensional descriptor is then passed to a batch - normalization layer [ reference ] and L2 - normalized to form the final template representation x template . section : GhostVLAD : NetVLAD with ghost clusters The key component of the aggregation block is our GhostVLAD trainable aggregation layer , which given N D F - dimensional face descriptors computes a single When considering only methods trained on the same data ( VGGFace2 ) , our improvement over the state - of - the - art is even larger : TAR at FAR=1E −5 of 0.762 vs 0.671 for verification on IJB - B , and TPIR at FPIR=0.01 of 0.776 vs 0.706 for verification on IJB - B. section : Analysis of ghost clusters Addition of ghost clusters was motivated by the intuition that it enables our network to learn to ignore uninformative low - quality images by assigning them to the discarded ghost clusters . We find empirically that the presence of such features improves the overall accuracy , thanks to the robustness of the built - in entities extractor . The problem of data sparsity is addressed by integrating features based on word clusters . More specifically , we use Brown clusters released by the authors of , as well as word clusters built from word2vec embeddings using k - means clustering . Researchers have also begun choosing designs that map well to GPU hardware to gain even more efficiency , including convolutional and locally connected networks , especially when optimized libraries like cuDNN and BLAS are available . Indeed , using high - performance computing infrastructure , it is possible today to train neural networks with more than 10 billion connections using clusters of GPUs . These results inspired us to focus first on making scalable design choices to efficiently utilize many GPUs before trying to engineer the algorithms and models themselves . This approach can be used to extend currently used third-party application signing mechanisms for increasing malware detection capabilities . In our work , we extract function calls from binaries in order to apply our clustering mechanism , called centroid . This method is capable of detecting unknown malwares . [ reference ] shows how each normalization step helps for 3D detection . We see that both frustum rotation ( such that frustum points have more similar XYZ distributions ) and mask centroid subtraction ( such that object points have smaller and more canonical XYZ ) are critical . In addition , extra alignment of object point cloud to object center by T - Net also contributes significantly to the performance . GPN introduces modules enabling simultaneous joint detection ( Sec . [ reference ] ) and dense joint - centroid regression ( Sec . [ reference ] ) , as shown in Figure [ reference ] . In this paper , considering the influence of community structure and the importance of community partition methods , we propose an improved community/susceptible/infective/removed ( CSIR ) information propagation model . In our improved CSIR model , we use two methods , uniform-center and cluster-center to improved K-means used in CSIR model . We compare the improved CSIR model with the original CSIR model , and analyze the importance of community structure . Then we greedily and iteratively choose the proposals which have most connections with others to estimate the centers . Although a cluster center proposal may only capture an object partially , its adjacent proposals ( i.e . , other proposals in the cluster ) can cover the whole object , or at worst contain larger parts of the object . Based on these proposal clusters , we propose two methods to refine instance classifiers ( object detectors ) during training . Here we set it to other numbers to explore its influence . The results from other numbers of - means clusters are mAP and CorLoc for clusters , and mAP and CorLoc for clusters , which are a little worse than the results from cluster . Therefore , we set the number of - means clusters to for the other experiments . Since we trust our current cluster center estimate more than older ones , we smooth the estimation by using an exponential moving average . The cluster center estimate at iteration t is obtained via µtk = αµ̂k During inference , threshold around each of the cluster centers to select all pixels belonging to the corresponding semantic class . This requires that the cluster centers of a specific class are the same in each image , which can be accomplished by coupling the cluster centers across a mini - batch . For the task of instance segmentation things are more complicated . The performance of each technique is evaluated through eleven well known benchmark functions . On the whole , the novel cluster based selection techniques are demonstrated to be extremely efficient and effective for achieving optimum solutions which are verified by simulated results . Clustering technique is an effective tool for medical data analysis as it can work for disease prediction , diagnosis record mining , medical image segmentation , and so on . This paper studies the kernel-based clustering method which can conduct nonlinear partition on input patterns and addresses two challenging issues in unsupervised learning environment : feature relevance estimate and cluster number selection . Specifically , a kernel-based competitive learning paradigm is presented for nonlinear clustering analysis .
Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization . Community detection based on overlapping nodes has become one of the most popular methods in recent years .
We consider the community detection problem in sparse random hypergraphs . Angelini et al. ( 2015 ) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model ( HSBM ) .
An efficient way to detect a community corresponding to a specific topic can greatly help users to obtain useful information . However , existing community detecting algorithms are all based on pages as units , the topic drift phenomenon becomes the inherent problem of each existing community detecting algorithm since the web pages always have multiple topics in content . This paper puts forward a new community detecting algorithm based on blocks as units , which combines the page dividing algorithm with the primary community detecting algorithm , overcoming the defect in the existing community detecting algorithms .
MethodsThe study draws on a unique dataset of transaction level data from an encrypted online drug market . Structural measures and community detection analysis are used to characterize and investigate the network structure . Exponential random graph modeling is used to evaluate which vendor characteristics explain variation in purchasing patterns .
Varying the Role of Attributes in Community Detection . Much of the community detection literature studies structural communities , communities defined solely by the connectivity patterns of the network . Often networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network .
A community detection algorithm is then proposed to recover community structure and to estimate communication probability parameters . It is verified that the community detection part converges in finite time , and the parameter estimation part converges almost surely . Simulations are given to illustrate algorithm performance .
While most works approach it as a simple categorization problem , sentiment analysis is actually a suitcase research problem that requires tackling many NLP tasks , including word polarity disambiguation , subjectivity detection , personality recognition , microtext normalization , concept extraction , time tagging , and aspect extraction . Sentiment analysis has raised growing interest both within the scientific community , leading to many exciting open challenges , as well as in the business world , due to the remarkable benefits to be had from financial and political forecasting , e - health and e - tourism , user profiling and community detection , manufacturing and supply chain applications , human communication comprehension and dialogue systems , etc . In the field of emotion recognition , early works by and showed that fusion of audio and visual systems , creating a bimodal signal , yielded a higher accuracy than any unimodal system .
Hierarchical clustering requires minimal parameters , has flexibility with similarity measure , and has strong visualization power , all of which makes it ideal for exploratory analysis . Hierarchical clustering is a particular branch of clustering algorithms where the results are not given as partitions , but rather a nested structure , which can represent the ordering among elements within a dataset . The study of the performance of hierarchical structure on time series data is limited .
We described our approach to the author clustering task at PAN 2017 , which resulted in the best-performing system at the aforementioned task . Our method performs a hierarchical clustering analysis using document features such as typed and untyped character n-grams , word n-grams , and stylometric features . We experimented with two feature representation methods , log-entropy model , and TF-IDF , while tuning minimum frequency threshold values to reduce the feature dimensionality .
Therefore , colorimetric information can improve the process . In this paper , we propose a hierarchical clustering based approach to extract dominant color masks of documents . Indeed , our dataset comprises different kind of scanned administrative document images such as invoices , forms , letters , and so on .
The second distinction lies in an efficient algorithm for obtaining a desirable profile , via exploring and exploiting multi-scale cluster structure hidden in but intrinsic to the data . The algorithm accomplishes its task with key components for lower-dimensional embedding with data-specific principal feature axes , hierarchical data clustering , multi-level matrix compression storage , and multi-level interaction computations . We provide experimental results from case studies with two important data analysis algorithms .
In Data clustering ( a sub field of Data mining ) , k-means and hierarchical based clustering algorithms are popular due to its excellent performance in clustering of large data sets . This paper presents two different comparative studies which includes various Data Clustering algorithms for analyzing best one with minimum clustering error .
One of the challenges in sensor networks , routing and transmission of data in networks , which is of high importance , So having a good routing algorithm can help to improve network performance . In this paper , a new method based on a hierarchical structure ( clustering ) is introduced in terms of three factors that are considered in the route selection discussion : Interference , energy consumption and deadline . In routing , when a packet is to be sent , the cluster heads depending on the stipulated time sends it .
In order to exploit the assumption of a hierarchical generative process over datasets we will use a ‘ parameter - transfer approach ’ [ see ][] transfer_survey to extend the variational autoencoder model of variational_autoencoder . figure Left : basic hierarchical model , where the plate encodes the fact that the context variable is shared across each item in a given dataset . Center : full neural statistician model with three latent layers .
A very natural and intuitive alternative organizes classifiers in a hierarchical manner according to the divide - and - conquer strategy . Although hierarchical classification has been proven effective for conventional linear classifiers , few attempts have been made to exploit category hierarchies in deep CNN models . Since deep CNN models are large models themselves , organizing them hierarchically imposes the following challenges .
In summary , this paper has the following contributions . First , we introduce a novel hierarchical architecture , called HD - CNN , for image classification . Second , we develop a scheme for learning the two - level organization of coarse and fine categories , and demonstrate various components of an HD - CNN can be independently pretrained .
The corresponding question - level feature is the LSTM hidden vector at time . Our hierarchical representation of the question is depicted in Fig . [ reference ] ( a ) .
[ reference ][ reference ] . Unfortunately , this kind of approach is not well suited to the case where different segments in the hierarchical decomposition have different lengths : for example , different words have different lengths , so a fixed hierarchy would not update its upper - level units in synchrony with the natural boundaries in the data . section : THE PROPOSED MODEL
We parametrize our convolution kernels in a similar flavor but in the spectral domain , which turns out to be straightforward and effective . Essentially , we find that multi - resolution analysis on graphs could be achieved without complicated hierarchical graph clustering . Before explaining what the exact parametrization is , we first discuss the intuition behind our design .
Gupta et al . use boundary detection and hierarchical grouping before performing category segmentation . The common attribute in all these approaches is the use of hand engineered features for classification of either RGB or RGBD images .
Spans exhaustive marking of text spans containing information relevant to the respective PIO categories ( Stage 1 annotation ) . Hierarchical labels assignment of more specific labels to subsequences comprising the marked relevant spans ( Stage 2 annotation ) . Repetition grouping of labeled tokens to indicate repeated occurrences of the same information ( Stage 2 annotation ) .
Given image and class embeddings , we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones ; zero - shot classification of an image proceeds by finding the label yielding the highest joint compatibility score . We use state - of - the - art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora . We establish a substantially improved state - of - the - art on the Animals with Attributes and Caltech - UCSD Birds datasets .
Its applicability is illustrated by comparing , on the one hand , some reduction-to-pole algorithms and on the other , the ( well-known ) k-means algorithm . Finally , the paper shows the results obtained by applying the Mean Shift algorithm as an alternative method to ‘ unsupervised clustering ’ of anomalies that appear in images obtained from geophysical data , in which the a priori knowledge of the number of classes is difficult or impossible .
Detecting communities has long been popular in the research on networks . It is usually modeled as an unsupervised clustering problem on graphs , based on heuristic assumptions about community characteristics , such as edge density and node homogeneity . In this work , we doubt the universality of these widely adopted assumptions and compare human labeled communities with machine predicted ones obtained via various mainstream algorithms .
Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets . We introduce unsupervised learning ( clustering ) where optimization is supported by the linguistic granules of context , thereby giving rise to so-called context-sensitive fuzzy clustering . The combination of neuro , evolutionary , and granular computing in the context of data mining is explored .
This paper presents a novel hardware Trojan detection technique in gate-level netlist based on the controllability and observability analyses . Using an unsupervised clustering analysis , the paper shows that the controllability and observability characteristics of Trojan gates present significant inter-cluster distance from those of genuine gates in a Trojan-inserted circuit , such that Trojan gates are easily distinguishable . The proposed technique does not require any golden model and can be easily integrated into the current integrated circuit design flow .
This paper presents an approach for improving performances of the unsupervised classification ( clustering ) in remote sensing imagery by proposing a technique to combine the classical techniques of K-means clustering and the Gaussian mixture model ( GMM ) with expectation-maximization ( EM ) . The proposed model means to apply firstly K-means clustering , and to use the result of this technique to compute means μk , covariance matrices Σk and mixing coefficients πk , considered as initialization parameters for the next stage of GMM-EM .
In this article , an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented . Rain blurs the wave signatures of radar images , and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures .
Relying on this , the representation learning and clustering for short text are seamlessly integrated into a unified framework . To further facilitate the model training process , we apply adversarial training to the unsupervised clustering setting , by adding perturbations to the cluster representations . The model parameters and perturbations are optimized alternately through a minimax game .
The Gamma Test ( GT ) is used to help facilitate the best use of limited data and to ensure that the ANNs are not over trained . The palynomorph database created with the greatest amount of salient features is then used with an unsupervised ANN clustering algorithm to predict groups within the dinoflagellate cysts . The results demonstrate that the system developed is able to achieve an average correct classification rate of > 90 % .
One such extension is to make the prior mean depend on higher-level task characteristics . More unsupervised clustering of tasks is obtained if we go from a single Gaussian prior to a mixture of Gaussians . This can be further generalized to a mixture of experts architecture with the gates depending on task characteristics .
In this context , present a generative model for image synthesis that yields a probabilistic segmentation of abnormalities . Another unsupervised technique is proposed by , a saliency - based method that exploits brain asymmetry in pathological cases . A common advantage of the above methods is that they do not require a training dataset with corresponding manual annotations .
Backpropagation through this fixed model modulates the loss on the learned embedding , increasing the gradient for initial embedding vectors whose instance membership is ambiguous and decreasing the gradient for embedding vectors that will be correctly resolved by the recurrent grouping phase . Figure [ reference ] shows a toy example highlighting the difference between supervised and unsupervised clustering . We generate a set of 1 - D data points drawn from three Gaussian distributions with mean and standard deviation as , and , respectively , as shown in Figure [ reference ]
We analyze the behavior of Gaussian Blurring Mean Shift ( GBMS ) with bandwidth as . If running GBMS for unsupervised clustering on these data with the default setting ( bandwidth is 0.2 ) , we can see they are grouped into three piles , as shown in Figure [ reference ] ( b ) .
For example , “ powerful , ” “ strong ” and “ Paris ” are equally distant . In this paper , we propose Paragraph Vector , an unsupervised algorithm that learns fixed - length feature representations from variable - length pieces of texts , such as sentences , paragraphs , and documents . Our algorithm represents each document by a dense vector which is trained to predict words in the document .
In this section , we discuss how the PixelGAN autoencoder can be viewed in the context of learning cross - domain relations between two different domains . We also describe how the problem of clustering or semi - supervised learning can be cast as the problem of finding a smooth cross - domain mapping from the data distribution to the categorical distribution . Recently several GAN - based methods have been developed to learn a cross - domain mapping between two different domains discogan , cyclegan , cross - domain - ilya , aae , cross - domain - nlp .
document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples .
Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose . Alignment methods exist on a supervision spectrum from requiring manually labeled fiducial points or landmarks , to requiring class labels , to fully unsupervised joint alignment and clustering models . Congealing is an unsupervised joint alignment method based on an entropy objective .
We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes .
However , their main drawback is their slowness . In this paper , a simple agglomerative clustering algorithm with a low computational complexity , is proposed . This method is especially convenient for performing clustering on large data sets , and could also be used as a linear time initialization method for other clustering algorithms , like the commonly used k-means algorithm .
Joint learning may allow for enhanced attribute classification when there is inherent dependency among the attributes . We propose an agglomerative clustering scheme to determine which sets of attributes should be learned jointly in order to maximize the margin of performance improvement . We evaluate the joint learning algorithm on a set of attributes for the task of person re-identification .
Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code . The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image , and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes . The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image .
A ground-truth strategy is employed to evaluate the accuracy of segmentation . The agglomerative based clustering and their ensemble version achieved the best clustering results . However they suffer from high run-time .
We study an agglomerative clustering problem motivated by visualizing disjoint glyphs ( represented by geometric shapes ) centered at specific locations on a geographic map . As we zoom out , the glyphs grow and start to overlap .
Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion . Agglomerative clustering and partially ordered sets of HOG features have been used to group images , and exemplars define a group as a set of random transformations applied to a single image . Note the latter does not scale easily , in particular to image segmentation where a single image would call for 40k classes .
The fraction of pairs of samples within a class among all possible pairs which are placed in the same cluster , over the total number of same - class pairs . Using these metrics , the F - score is computed as : The simplest way we found to demonstrate the effectiveness of our deep features and the proposed TPE method , is to use the standard MATLAB implementation of the agglomerative clustering algorithm with the average linkage metric . We use the cosine similarity as our basic clustering metric .
In many applications of document clustering , a document may include multiple topics and thus may relate to multiple categories at the same time . Most of the existing subspace clustering algorithms can only perform hard clustering on document collections . In this paper , a fuzzy algorithm named R-FPC is introduced for document clustering .
Unsupervised learning algorithms manage only unlabeled data . 1.1 The clustering problem Clustering is the most important unsupervised learning problem , and cluster analysis is used in a variety of scientific disciplines such as biology , psychology , medicine , marketing , computer vision and many others . Organizing data into coherent groupings is indeed one of the most fundamental modes of understanding and learning .
Land use zoning is essentially a classification problem , so how to apply the idea of fuzzy classification to discuss the optimal regionalization number strategy of land use shall be valuable for providing decision assistance for regional land use optimal zoning . But current research on land use zoning is usually conducted by adoption of traditional hard classification clustering method , of which the classification number is of a strong subjectivity , ignoring the existence of the objectivity of optimal zoning number . This study proposed a quantitative calculation method for land use optimal regionalization number by applying factor analysis , c-means and fuzzy c-means algorithm complying with the efficient maximum membership principle .
document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . The model discovers clusters that accurately match semantic classes , achieving state - of - the - art results in eight unsupervised clustering benchmarks spanning image classification and segmentation .
At the same time , these clusters are exposed to an increasing number of attacks coming from public networks . Therefore , mechanisms for efficiently and flexibly managing security have now become an essential requirement for clusters . However , despite the growing importance of cluster security , this field has been only minimally addressed by contemporary cluster administration techniques .
In this paper , we propose a novel deep network for WSOD . Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning ( MIL ) , our strategy generates proposal clusters to learn refined instance classifiers by an iterative process . The proposals in the same cluster are spatially adjacent and associated with the same object .
We have proposal clusters according to ( for background and others for objects ) . For object clusters , the - th cluster consists of proposal boxes , an object label that is the same as the cluster center label , and a confidence score that is the same as the cluster center score , where indicates the confidence that corresponds to an object of class . Unlike object clusters , the background cluster consists of proposals and a label indicating the background .
In practice , however , there are significant algorithmic and performance challenges . In this work , we address these challenges and finally realize the promise of conditional computation , achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters . We introduce a Sparsely - Gated Mixture - of - Experts layer ( MoE ) , consisting of up to thousands of feed - forward sub - networks .
We stick to the simplest parsers in each category – greedy inference for the transition - based architecture , and a first - order , arc - factored model for the graph - based architecture . Despite the simplicity of the parsing architectures and the feature functions , we achieve near state - of - the - art parsing accuracies in both English ( 93.1 UAS ) and Chinese ( 86.6 UAS ) , using a first - order parser with two features and while training solely on Treebank data , without relying on semi - supervised signals such as pre - trained word embeddings , word - clusters , or techniques such as tri - training . When also including pre - trained word embeddings , we obtain further improvements , with accuracies of 93.9 UAS ( English ) and 87.6 UAS ( Chinese ) for a greedy transition - based parser with 11 features , and 93.6 UAS ( En ) / 87.4 ( Ch ) for a greedy transition - based parser with 4 features .
The compact D - dimensional descriptor is then passed to a batch - normalization layer [ reference ] and L2 - normalized to form the final template representation x template . section : GhostVLAD : NetVLAD with ghost clusters The key component of the aggregation block is our GhostVLAD trainable aggregation layer , which given N D F - dimensional face descriptors computes a single
When considering only methods trained on the same data ( VGGFace2 ) , our improvement over the state - of - the - art is even larger : TAR at FAR=1E −5 of 0.762 vs 0.671 for verification on IJB - B , and TPIR at FPIR=0.01 of 0.776 vs 0.706 for verification on IJB - B. section : Analysis of ghost clusters Addition of ghost clusters was motivated by the intuition that it enables our network to learn to ignore uninformative low - quality images by assigning them to the discarded ghost clusters .
We find empirically that the presence of such features improves the overall accuracy , thanks to the robustness of the built - in entities extractor . The problem of data sparsity is addressed by integrating features based on word clusters . More specifically , we use Brown clusters released by the authors of , as well as word clusters built from word2vec embeddings using k - means clustering .
Researchers have also begun choosing designs that map well to GPU hardware to gain even more efficiency , including convolutional and locally connected networks , especially when optimized libraries like cuDNN and BLAS are available . Indeed , using high - performance computing infrastructure , it is possible today to train neural networks with more than 10 billion connections using clusters of GPUs . These results inspired us to focus first on making scalable design choices to efficiently utilize many GPUs before trying to engineer the algorithms and models themselves .
This approach can be used to extend currently used third-party application signing mechanisms for increasing malware detection capabilities . In our work , we extract function calls from binaries in order to apply our clustering mechanism , called centroid . This method is capable of detecting unknown malwares .
[ reference ] shows how each normalization step helps for 3D detection . We see that both frustum rotation ( such that frustum points have more similar XYZ distributions ) and mask centroid subtraction ( such that object points have smaller and more canonical XYZ ) are critical . In addition , extra alignment of object point cloud to object center by T - Net also contributes significantly to the performance .
GPN introduces modules enabling simultaneous joint detection ( Sec . [ reference ] ) and dense joint - centroid regression ( Sec . [ reference ] ) , as shown in Figure [ reference ] .
In this paper , considering the influence of community structure and the importance of community partition methods , we propose an improved community/susceptible/infective/removed ( CSIR ) information propagation model . In our improved CSIR model , we use two methods , uniform-center and cluster-center to improved K-means used in CSIR model . We compare the improved CSIR model with the original CSIR model , and analyze the importance of community structure .
Then we greedily and iteratively choose the proposals which have most connections with others to estimate the centers . Although a cluster center proposal may only capture an object partially , its adjacent proposals ( i.e . , other proposals in the cluster ) can cover the whole object , or at worst contain larger parts of the object . Based on these proposal clusters , we propose two methods to refine instance classifiers ( object detectors ) during training .
Here we set it to other numbers to explore its influence . The results from other numbers of - means clusters are mAP and CorLoc for clusters , and mAP and CorLoc for clusters , which are a little worse than the results from cluster . Therefore , we set the number of - means clusters to for the other experiments .
Since we trust our current cluster center estimate more than older ones , we smooth the estimation by using an exponential moving average . The cluster center estimate at iteration t is obtained via µtk = αµ̂k
During inference , threshold around each of the cluster centers to select all pixels belonging to the corresponding semantic class . This requires that the cluster centers of a specific class are the same in each image , which can be accomplished by coupling the cluster centers across a mini - batch . For the task of instance segmentation things are more complicated .
The performance of each technique is evaluated through eleven well known benchmark functions . On the whole , the novel cluster based selection techniques are demonstrated to be extremely efficient and effective for achieving optimum solutions which are verified by simulated results .
Clustering technique is an effective tool for medical data analysis as it can work for disease prediction , diagnosis record mining , medical image segmentation , and so on . This paper studies the kernel-based clustering method which can conduct nonlinear partition on input patterns and addresses two challenging issues in unsupervised learning environment : feature relevance estimate and cluster number selection . Specifically , a kernel-based competitive learning paradigm is presented for nonlinear clustering analysis .
$\mathcal{K}$ -Lowest-Influence Overlapping Nodes Based Community Detection in Complex Networks Community Detection in the Sparse Hypergraph Stochastic Block Model An Approach of Community Detecting Based on Block Level Link Analysis The Network Structure of Opioid Distribution on a Darknet Cryptomarket A Map Equation with Metadata: Varying the Role of Attributes in Community Detection Community Detection for Gossip Dynamics with Stubborn Agents Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling Evaluation of Hierarchical Structures for Time Series Data Hierarchical Clustering Analysis: The Best-Performing Approach at PAN 2017 Author Clustering Task Dominant color segmentation of administrative document images by hierarchical clustering Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering Error Evaluation on K- Means and Hierarchical Clustering with Effect of Distance Functions for Iris Dataset A New Method for Hierarchical Routing in Wireless Sensor Networks by Considering the Packet Deadline Towards a Neural Statistician HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Hierarchical Question-Image Co-Attention for Visual Question Answering Hierarchical Multiscale Recurrent Neural Networks SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature Evaluation of output embeddings for fine-grained image classification MEAN SHIFT: A NON-PARAMETRIC ALGORITHM FOR THE SEGMENTATION OF ANOMALIES IN GEOPHYSICAL IMAGES OBTAINED FROM MAGNETIC PROSPECTION DATA CONE: Community Oriented Network Embedding Fuzzy computing for data mining COTD: Reference-Free Hardware Trojan Detection and Recovery Based on Controllability and Observability in Gate-Level Netlist Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery Identification of Rain and Low-Backscatter Regions in X-Band Marine Radar Images: An Unsupervised Approach Attentive Representation Learning with Adversarial Training for Short Text Clustering The semi-automated classification of sedimentary organic matter and dinoflagellate cysts in palynological preparations Task Clustering and Gating for Bayesian Multitask Learning Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Recurrent Pixel Embedding for Instance Grouping Distributed Representations of Sentences and Documents PixelGAN Autoencoders Invariant Information Clustering for Unsupervised Image Classification and Segmentation Do Convnets Learn Correspondence? Joint maximum purity forest with application to image super-resolution An Agglomerative Clustering Method for Large Data Sets On the Exploration of Joint Attribute Learning for Person Re-identification Natural Image Segmentation with Adaptive Texture and Boundary Encoding Address block segmentation using ensemble-clustering techniques A Practical Algorithm for Spatial Agglomerative Clustering Invariant Information Clustering for Unsupervised Image Classification and Segmentation Triplet probabilistic embedding for face verification and clustering A Robust Algorithm for Fuzzy Document Clustering Transductional Dominant-Set Clustering using Euler Kernels ANALYSIS ON REGIONAL LAND USE ZONING BASED ON FUZZY C-MEANS ALGORITHM OF PRINCIPAL COMPONENTS ——A CASE STUDY OF DAPU COUNTY IN GUANGDONG PROVINCE Invariant Information Clustering for Unsupervised Image Classification and Segmentation Message from the organising committee PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations GhostVLAD for set-based face recognition Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Deep Speech: Scaling up end-to-end speech recognition Detecting Symbian OS malware through static function call analysis Frustum PointNets for 3D Object Detection from RGB-D Data Generative Partition Networks for Multi-Person Pose Estimation An improved CSIR information propagation model in social networks PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Learning Deep Parsimonious Representations Semantic Instance Segmentation with a Discriminative Loss Function PERFORMANCE EVALUATION OF NOVEL SELECTION PROCESSES THROUGH HYBRIDIZATION OF K-MEANS CLUSTERING AND GENETIC ALGORITHM Feature Weighted Kernel Clustering with Application to Medical Data Analysis
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Knowl. Based Syst. CLEF ACM Symposium on Document Engineering ICLR 2015 IEEE International Conference on Computer Vision (ICCV) NIPS ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence ACL 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IGARSS Medical Image Anal. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML NIPS 2019 IEEE/CVF International Conference on Computer Vision (ICCV) NIPS J. Electronic Imaging ACCV (1) ACCV (1) CompSysTech ALENEX 2019 IEEE/CVF International Conference on Computer Vision (ICCV) 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) AINA Workshops 2019 IEEE/CVF International Conference on Computer Vision (ICCV) IEEE Transactions on Pattern Analysis and Machine Intelligence ICLR Transactions of the Association for Computational Linguistics ACCV ArXiv ArXiv MALWARE 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv CITS IEEE Transactions on Pattern Analysis and Machine Intelligence NIPS ArXiv Brain and Health Informatics
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