With the explosive growth of web-based cameras and mobile devices , billions of photographs are uploaded to the internet . We can trivially collect a huge number of photo streams for various goals , such as image clustering , 3D scene reconstruction , and other big data applications . However , such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives , resolutions , lighting , noises , and distortions . Image similarity measurement is very important part for image clustering and content based image retrieval . Store the images and searching them with efficiency is the main issue . This paper proposes an improved form of the ant lion optimization algorithm ( IALO ) to solve image clustering problem . The improvement of the algorithm was made using a new boundary decreasing procedure . However , prestack data contains excessive redundancy , so it is critical to effectively extract features from prestack seismic data . In this paper , the seismic facies recognition of prestack seismic data is considered as an image clustering problem in computer vision ( CV ) . Each prestack data gather is treated as a picture , using a 2D convolutional auto-encoder network ( 2DCAE ) to extract the features of the pre-stack data and eliminate the redundancy . The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning . In order to involve user in image data clustering , we proposed in [ 1 ] a new approach for interactive semi-supervised clustering that translates user feedback ( expressed at the level of individual images ) into pairwise constraints between groups of images , these groups being formed thanks to the underlying hierarchical clustering solution and user feedback . Recently , the need for appropriate measures of distance or similarity between data led to the emergence of distance metric learning approaches . This paper presents another approach , commonality based MIC . In the case of image-set clustering , images preserving strong common local features form a cluster . In this approach , image variations that do not break the common features do not affect the clustering result . Image-set clustering is a problem decomposing a given image set into disjoint subsets satisfying specified criteria . For single vector image representations , proximity or similarity criterion is widely applied , i.e. , proximal or similar images form a cluster . This paper presents a study on area change detection applications based on remote sensing data . The crucial parts of the process are in selecting the optimal combination of bands and in the image clustering process , so that we could obtain the object regions correctly . The proposed methodology consists of the following steps : ( i ) image band selection using Optimum Index Factor ; ( ii ) K-Means clustering where their cluster centres are determined by K-D tree ; and ( iii ) detecting area changes . This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images , which have the same color as the pre-selected pixels with background subtraction . In the software development testing we examine image based clustering , as we can used clustering by distance base , by pixel ( red , green , blue ) value etc . , The problem is solved by region based method which is based on connect component and background detection techniques . The appropriate Java codes are developed for solve this task . Recently , randomized algorithms have received a great deal of attentions in big data analysis . This paper presents a randomized algorithm for accurate feature selection in image clustering analysis . The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database . We extend PLSA to help transfer the knowledge from social Web data , which have mixed feature representations . Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising . Since we have explored both the spatial and multi-feature contextual information , more representative sparse codes of the visual phrases can be obtained . The experiments on image pattern discovery , image scene clustering and image classification justify the advantages of the proposed algorithm . First we introduce a generic objective , Invariant Information Clustering , which can be used to cluster any kind of unlabelled paired data by training a network to predict cluster identities ( s : generic_clustering ) . We then apply it to image clustering ( s : image_clustering , f : overview and f : mnist_dots ) and segmentation ( s : This would not be possible without pairing samples . subsection : Image clustering IIC requires a source of paired samples , which are often unavailable in unsupervised image clustering applications . With the changes of illumination , action and background , face clustering is a challenging task that demands accuracy and robustness . In order to improve the face clustering performance in videos , we propose a method which considers the available prior knowledge , multi-view and constrained information . It is a challenging problem due to the huge variation in the appearance of each character . Although existing methods demonstrate promising results in clean environment , the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process . In this paper we present two schemes of global face-name matching based framework for robust character identification . Automatic face identification of characters in videos is a challenging problem due to the huge variation in the appearance of each character . Although existing methods demonstrate promising results in the clean environment , the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process . In this system , two schemes of a global face-name matching based framework are proposed for robust character identification . Thus both of the accuracy and generalization of clustering are improved simultaneously . The experimental results on the AR face databases show that the proposed ESSC not only makes up to 9.0 % improvements on accuracy compared with SSC , but also is more robust and scalable for dealing with larger face clustering problems ( up to 400 samples from 100 subjects ) under variant expressions , disguises or illuminations . Over the past few decades , significant progress has been made in clustering high-dimensional data sets distributed around a collection of linear and affine subspaces . This article presented a review of such progress , which included a number of existing subspace clustering algorithms together with an experimental evaluation on the motion segmentation and face clustering problems in computer vision . Unlike pbM , where the scale of the inlier noise is estimated simultaneously with the model parameters , gpbM has three distinct stages – scale estimation , robust model estimation and inlier/outlier dichotomy . We evaluate our performance on challenging synthetic data , face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset . Results of state-of-the-art methods are presented for comparison . We alternately learn the opti- mal subspace and the clusters . Experimental results demon- strate that the proposed approach performs better than other learning methods for face image clustering . © 2009 Society of Photo-Optical Instrumentation Engineers . In this paper , a modified Local Binary Pattern ( LBP ) method is applied for human face feature extraction and features are computed from micro patterns created using modified LBP from human faces . Face cluster classification as well as recognition is done by Multilayer Feed Forward Neural Network including back propagation algorithm for learning . In the present study , both real life male and female face images are captured and taken for implementation and an improved result has been obtained . By constructing sub-graphs around each instance as input data , which depict the local context , we utilize the graph convolution network ( GCN ) to perform reasoning and infer the likelihood of linkage between pairs in the sub-graphs . Experiments show that our method is more robust to the complex distribution of faces than conventional methods , yielding favorably comparable results to state-of-the-art methods on standard face clustering benchmarks , and is scalable to large datasets . Furthermore , we show that the proposed method does not need the number of clusters as prior , is aware of noises and outliers , and can be extended to a multi-view version for more accurate clustering accuracy . After summarized and analyzed such a thinking , this paper has discussed the pros and cons of face data clustering by using the traditional K-means clustering algorithm . In addition , this paper has further proposed an online weight-based clustering algorithm ( OWCA for short ) that strengthens the advantages of face data clustering , restricts the computational complexity and develops an online processing scheme . Experimental results on the ORL face library demonstrated the correctness and effectiveness of OWCA . The face abstracting method comprises the steps of generating face images of different persons appearing in an original video and forming an arisen face image list . The method further comprises the steps of face detection , face feature extraction , face feature clustering , face abstract image generation and the like , wherein the face detection is used for scanning image frames in the original video and judging whether face areas exist in the video frames . The invention further discloses a device for implementing the face abstracting method . document : FaceNet : A Unified Embedding for Face Recognition and Clustering Despite significant recent advances in the field of face recognition , implementing face verification and recognition efficiently at scale presents serious challenges to current approaches . The detailed evaluation on our personal photos test set is shown in Figure [ reference ] . While the largest model achieves a dramatic improvement in accuracy compared to the tiny NNS2 , the latter can be run 30ms / image on a mobile phone and is still accurate enough to be used in face clustering . The sharp drop in the ROC for indicates noisy labels in the test data groundtruth . DeepId2 + achieved 93.2 % and our method reduces this error by 30 % , comparable to our improvement on LFW . subsection : Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity . In this work , we propose a distributed algorithm that is integrated online K-Means user contexts clustering with online learning mechanisms for selecting a highlighted news . Our proposed algorithm for online clustering with lower bound confident clustering approximates closer to offline K-Means clusters than greedy clustering and gives better performance in learning process . The algorithm provides a scalability , cheap storage and computation cost approach for large scale news recommendation systems . This sketch allows us to estimate L2 ( Euclidean ) distances and dotproducts with high accuracy . We verify the validity of this sketch by applying it to an online clustering problem , where we compare our results to the offline algorithm and an existing L2 sketch , and observe comparable results in terms of accuracy , and a reduced runtime cost . Preliminary experiments were carried out on synthetic and selected benchmark datasets to evaluate the performance of the proposed system . Promising results were obtained , which indicate the viability of spike-based population coding for online data clustering . Competitive learning neural networks are regarded as a powerful tool for online data clustering to represent a non-stationary probability distribution with a fixed number of weight vectors . One difficulty in practical applications of competitive learning neural networks to online data clustering is that most of them require heuristically-predetermined threshold parameters for balancing a trade-off between convergence accuracy , i.e. error minimization performance , and speed of adaptation to the changes in source statistics . Although adaptation acceleration is achievable by relocating a " useless " node so that it becomes useful , excessive relocation often disturbs error minimization . We present a decentralized algorithm for online clustering analysis used for anomaly detection in self-monitoring distributed systems . In particular , we demonstrate the monitoring of a network of printing devices that can perform the analysis without the use of external computing resources ( i.e. in-network analysis ) . There are three major challenges for the new algorithm design . The first challenge is online clustering computation . Most of the existing spectral methods on evolving networks are off-line methods , using standard eigensystem solvers such as the Lanczos method . The experiment results show that the use of Wikipedia as external knowledge and text semantic similarity improves the speed and accuracy of the meme clustering problem when comparing to baselines . For the online clustering process , using a damped window model approach is suitable for online streaming environments as it not only requires low prediction and training costs , but also assigns more weight to recent data and popular topics . Based on classical k-means method , by analysis and solving the new adding samples of dynamical training data set , local renew or global clustering is performed by the changing range of objective function , and the dynamical data are clustered online . The speed of classical k-means algorithm is slow by the reiterative clustering is needed of every online clustering step , but the speed of Dynamical K-means algorithm is accelerated . Simulation results on standard and artificial social network datasets demonstrate that comparing with classical k-means clustering means , the excellent clustering results can be obtained by this method and the concept drifting phenomenon can be monitored efficiently . The goal of the work is to create a new approach to solving the tasks of clustering in data streams when information is fed observation-by-observation in online mode . The following tasks were solved in the article : the model of neural network ensembles for data clustering was created , the methods of data clustering to process mass data were developed , the methods of online data clustering of data using neural network ensembles working in the parallel mode were developed . The following results were obtained : the operation principles of the ensembles of the Kohonen neural network were formulated and practical requirements for dealing with mass data were specified . This paper proposes exploiting static and dynamically mined metadata for enriching web searching with exploration services . Online results clustering , which is a mining task of dynamic nature since it is based on query-dependent snippets , is useful for providing users with overviews of the top results and thus allowing them to restrict their focus to the desired parts . On the other hand , the various static metadata that are available to a search engine ( e.g. , domain , language , date , and filetype ) are commonly exploited only through the advanced ( form-based ) search facilities that some WSEs offer ( and users rarely use ) . Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task . Moreover , we also conduct computational complexity analysis and the result shows that MMRSC can effective handle the multi-view clustering problem without increasing the computational cost as compared to GraphSC . Existing algorithms do not meet the requirements of multi-view image clustering under big data conditions . Here , we design a multi-view clustering algorithm for massive and unstructured images . Experiments are performed using multiple datasets . The results show that the proposed algorithm is capable of multi-view clustering of image data with high accuracy , low error rate and favorable stability . In this paper , we address the multi-view subspace clustering problem . Our method utilize the circulant algebra for tensor , which is constructed by stacking the subspace representation matrices of different views and then shifting , to explore the high order correlations underlying multi-view data . In this paper , we address the multi-view subspace clustering problem . Our method utilizes the circulant algebra for tensor , which is constructed by stacking the subspace representation matrices of different views and then rotating , to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved , as well as the high order correlations underlying multi-view data can be explored . With the ability to exploit the internal structure of data , graph-based models have received a lot of attention and have achieved great success in multiview subspace clustering for multimedia data . Most of the existing methods individually construct an affinity graph for each single view and fuse the result obtained from each single graph . In this paper , we explore the problem of multiview subspace clustering . We introduce a low-rank tensor constraint to explore the complementary information from multiple views and , accordingly , establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering ( LT-MSC ) . Abstract Multi-view subspace clustering is essential to many scientific problems . However , most existing methods suffer from three aspects of issues . In recent years many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed . In this paper , we propose a model for multiple view unsupervised clustering based on Kohonen self-organizing-map algorithm . The results of simulations in two dimensional space using three views of training sets having different statistical properties have been presented . Many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed . In this paper we have proposed a model for multiple view unsupervised clustering based on Kohonen self-organizing-map method . Learning from multi-view data is important in many applications . In this paper , we propose a novel convex subspace representation learning method for unsupervised multi-view clustering . We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm . Self-representation model has made good progress for a single view subspace clustering . This paper proposed the multi-view subspace clustering model based on self-representation . The idea is to learn the model iteratively from easy to complex samples in a self - paced fashion . By virtue of its generality , the SPL theory has been widely applied to various tasks , such as multi - view clustering [ reference ] , multi - label propagation [ reference ] , multimedia event detection , and co - saliency detection . More recently , introduced the pre - defined learning curriculum to the conventional self - paced learning regime which can take into account both the helpful prior knowledge known before training and the self - learning progress during training . Our model , called Comraf * ( pronounced Comraf-Star ) , efficiently incorporates various views in multi-modal clustering , by which it allows great modeling flexibility . Comraf Finally , ( c ) a two-phase block-incremental tensor decomposition technique , BICP , efficiently and effectively maintains tensor decomposition results in the presence of incrementally evolving tensor data . We also present experimental results , with diverse data sets , that show that , if properly constructed , sub-tensor impact graphs can indeed help overcome various density and noise challenges in clustering of multi-modal data sets . Abstract Multi-modal canonical correlation analysis ( MCCA ) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data . MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data . Identifying possible threat actors from samples of malware remains an active area of research with important ramifications for cybersecruity practitioners . The unsupervised identification and characterization of malware samples has been primarily treated as an early integration , multi-modal clustering problem where all possible features derived from the samples are concatenated into one feature vector , which can then be fed into a standard unsupervised learning algorithm . In this work , we focus on characterizing malware samples into possible threat actors from both late integration and intermediate integration multi-modal data perspectives . However , previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data , typically in multi-modal contexts . In this paper , we propose a new technique to accelerate the calculation of shared neighbours and we introduce a new multi-source shared neighbours scheme applied to multi-modal image clustering . We first extend existing SNN-based similarity measures to the case of multiple sources and we introduce an original automatic source selection step when building candidate clusters . Generalizations to multiple types are non-trivial , plagued by challenges such as choice of types and numbers of models , sampling imbalance and parameter tuning . In this work , we formulate the multi-type subspace clustering problem as one of learning non-linear subspace filters via deep multi-layer perceptrons ( mlps ) . The response to the learnt subspace filters serve as the feature embedding that is clustering-friendly , i.e. , points of the same clusters will be embedded closer together through the network . Moreover , most subspace multi-clustering methods are especially scalable for high-dimensional data , which has become more and more popular in real applications due to the advances of big data technologies . In this paper , we focus on the subject of subspace multi-clustering , which has not been reviewed by any previous survey . We formulate the subspace multi-clustering problem and categorize the methodologies in different perspectives ( e.g. , de-coupled methods and coupled methods ) . We compare different methods on a series of specific properties ( e.g. , input parameters and different kinds of subspaces ) and analyze the advantages and disadvantages . We present convolutional neural network based approaches for unsupervised multimodal subspace clustering . The proposed framework consists of three main stages — multimodal encoder , self-expressive layer , and multimodal decoder .
With the explosive growth of web-based cameras and mobile devices , billions of photographs are uploaded to the internet . We can trivially collect a huge number of photo streams for various goals , such as image clustering , 3D scene reconstruction , and other big data applications . However , such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives , resolutions , lighting , noises , and distortions .
Image similarity measurement is very important part for image clustering and content based image retrieval . Store the images and searching them with efficiency is the main issue .
This paper proposes an improved form of the ant lion optimization algorithm ( IALO ) to solve image clustering problem . The improvement of the algorithm was made using a new boundary decreasing procedure .
However , prestack data contains excessive redundancy , so it is critical to effectively extract features from prestack seismic data . In this paper , the seismic facies recognition of prestack seismic data is considered as an image clustering problem in computer vision ( CV ) . Each prestack data gather is treated as a picture , using a 2D convolutional auto-encoder network ( 2DCAE ) to extract the features of the pre-stack data and eliminate the redundancy .
The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning . In order to involve user in image data clustering , we proposed in [ 1 ] a new approach for interactive semi-supervised clustering that translates user feedback ( expressed at the level of individual images ) into pairwise constraints between groups of images , these groups being formed thanks to the underlying hierarchical clustering solution and user feedback . Recently , the need for appropriate measures of distance or similarity between data led to the emergence of distance metric learning approaches .
This paper presents another approach , commonality based MIC . In the case of image-set clustering , images preserving strong common local features form a cluster . In this approach , image variations that do not break the common features do not affect the clustering result .
Image-set clustering is a problem decomposing a given image set into disjoint subsets satisfying specified criteria . For single vector image representations , proximity or similarity criterion is widely applied , i.e. , proximal or similar images form a cluster .
This paper presents a study on area change detection applications based on remote sensing data . The crucial parts of the process are in selecting the optimal combination of bands and in the image clustering process , so that we could obtain the object regions correctly . The proposed methodology consists of the following steps : ( i ) image band selection using Optimum Index Factor ; ( ii ) K-Means clustering where their cluster centres are determined by K-D tree ; and ( iii ) detecting area changes .
This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images , which have the same color as the pre-selected pixels with background subtraction . In the software development testing we examine image based clustering , as we can used clustering by distance base , by pixel ( red , green , blue ) value etc . , The problem is solved by region based method which is based on connect component and background detection techniques . The appropriate Java codes are developed for solve this task .
Recently , randomized algorithms have received a great deal of attentions in big data analysis . This paper presents a randomized algorithm for accurate feature selection in image clustering analysis . The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database .
We extend PLSA to help transfer the knowledge from social Web data , which have mixed feature representations . Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising .
Since we have explored both the spatial and multi-feature contextual information , more representative sparse codes of the visual phrases can be obtained . The experiments on image pattern discovery , image scene clustering and image classification justify the advantages of the proposed algorithm .
First we introduce a generic objective , Invariant Information Clustering , which can be used to cluster any kind of unlabelled paired data by training a network to predict cluster identities ( s : generic_clustering ) . We then apply it to image clustering ( s : image_clustering , f : overview and f : mnist_dots ) and segmentation ( s :
This would not be possible without pairing samples . subsection : Image clustering IIC requires a source of paired samples , which are often unavailable in unsupervised image clustering applications .
With the changes of illumination , action and background , face clustering is a challenging task that demands accuracy and robustness . In order to improve the face clustering performance in videos , we propose a method which considers the available prior knowledge , multi-view and constrained information .
It is a challenging problem due to the huge variation in the appearance of each character . Although existing methods demonstrate promising results in clean environment , the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process . In this paper we present two schemes of global face-name matching based framework for robust character identification .
Automatic face identification of characters in videos is a challenging problem due to the huge variation in the appearance of each character . Although existing methods demonstrate promising results in the clean environment , the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process . In this system , two schemes of a global face-name matching based framework are proposed for robust character identification .
Thus both of the accuracy and generalization of clustering are improved simultaneously . The experimental results on the AR face databases show that the proposed ESSC not only makes up to 9.0 % improvements on accuracy compared with SSC , but also is more robust and scalable for dealing with larger face clustering problems ( up to 400 samples from 100 subjects ) under variant expressions , disguises or illuminations .
Over the past few decades , significant progress has been made in clustering high-dimensional data sets distributed around a collection of linear and affine subspaces . This article presented a review of such progress , which included a number of existing subspace clustering algorithms together with an experimental evaluation on the motion segmentation and face clustering problems in computer vision .
Unlike pbM , where the scale of the inlier noise is estimated simultaneously with the model parameters , gpbM has three distinct stages – scale estimation , robust model estimation and inlier/outlier dichotomy . We evaluate our performance on challenging synthetic data , face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset . Results of state-of-the-art methods are presented for comparison .
We alternately learn the opti- mal subspace and the clusters . Experimental results demon- strate that the proposed approach performs better than other learning methods for face image clustering . © 2009 Society of Photo-Optical Instrumentation Engineers .
In this paper , a modified Local Binary Pattern ( LBP ) method is applied for human face feature extraction and features are computed from micro patterns created using modified LBP from human faces . Face cluster classification as well as recognition is done by Multilayer Feed Forward Neural Network including back propagation algorithm for learning . In the present study , both real life male and female face images are captured and taken for implementation and an improved result has been obtained .
By constructing sub-graphs around each instance as input data , which depict the local context , we utilize the graph convolution network ( GCN ) to perform reasoning and infer the likelihood of linkage between pairs in the sub-graphs . Experiments show that our method is more robust to the complex distribution of faces than conventional methods , yielding favorably comparable results to state-of-the-art methods on standard face clustering benchmarks , and is scalable to large datasets . Furthermore , we show that the proposed method does not need the number of clusters as prior , is aware of noises and outliers , and can be extended to a multi-view version for more accurate clustering accuracy .
After summarized and analyzed such a thinking , this paper has discussed the pros and cons of face data clustering by using the traditional K-means clustering algorithm . In addition , this paper has further proposed an online weight-based clustering algorithm ( OWCA for short ) that strengthens the advantages of face data clustering , restricts the computational complexity and develops an online processing scheme . Experimental results on the ORL face library demonstrated the correctness and effectiveness of OWCA .
The face abstracting method comprises the steps of generating face images of different persons appearing in an original video and forming an arisen face image list . The method further comprises the steps of face detection , face feature extraction , face feature clustering , face abstract image generation and the like , wherein the face detection is used for scanning image frames in the original video and judging whether face areas exist in the video frames . The invention further discloses a device for implementing the face abstracting method .
document : FaceNet : A Unified Embedding for Face Recognition and Clustering Despite significant recent advances in the field of face recognition , implementing face verification and recognition efficiently at scale presents serious challenges to current approaches .
The detailed evaluation on our personal photos test set is shown in Figure [ reference ] . While the largest model achieves a dramatic improvement in accuracy compared to the tiny NNS2 , the latter can be run 30ms / image on a mobile phone and is still accurate enough to be used in face clustering . The sharp drop in the ROC for indicates noisy labels in the test data groundtruth .
DeepId2 + achieved 93.2 % and our method reduces this error by 30 % , comparable to our improvement on LFW . subsection : Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity .
In this work , we propose a distributed algorithm that is integrated online K-Means user contexts clustering with online learning mechanisms for selecting a highlighted news . Our proposed algorithm for online clustering with lower bound confident clustering approximates closer to offline K-Means clusters than greedy clustering and gives better performance in learning process . The algorithm provides a scalability , cheap storage and computation cost approach for large scale news recommendation systems .
This sketch allows us to estimate L2 ( Euclidean ) distances and dotproducts with high accuracy . We verify the validity of this sketch by applying it to an online clustering problem , where we compare our results to the offline algorithm and an existing L2 sketch , and observe comparable results in terms of accuracy , and a reduced runtime cost .
Preliminary experiments were carried out on synthetic and selected benchmark datasets to evaluate the performance of the proposed system . Promising results were obtained , which indicate the viability of spike-based population coding for online data clustering .
Competitive learning neural networks are regarded as a powerful tool for online data clustering to represent a non-stationary probability distribution with a fixed number of weight vectors . One difficulty in practical applications of competitive learning neural networks to online data clustering is that most of them require heuristically-predetermined threshold parameters for balancing a trade-off between convergence accuracy , i.e. error minimization performance , and speed of adaptation to the changes in source statistics . Although adaptation acceleration is achievable by relocating a " useless " node so that it becomes useful , excessive relocation often disturbs error minimization .
We present a decentralized algorithm for online clustering analysis used for anomaly detection in self-monitoring distributed systems . In particular , we demonstrate the monitoring of a network of printing devices that can perform the analysis without the use of external computing resources ( i.e. in-network analysis ) .
There are three major challenges for the new algorithm design . The first challenge is online clustering computation . Most of the existing spectral methods on evolving networks are off-line methods , using standard eigensystem solvers such as the Lanczos method .
The experiment results show that the use of Wikipedia as external knowledge and text semantic similarity improves the speed and accuracy of the meme clustering problem when comparing to baselines . For the online clustering process , using a damped window model approach is suitable for online streaming environments as it not only requires low prediction and training costs , but also assigns more weight to recent data and popular topics .
Based on classical k-means method , by analysis and solving the new adding samples of dynamical training data set , local renew or global clustering is performed by the changing range of objective function , and the dynamical data are clustered online . The speed of classical k-means algorithm is slow by the reiterative clustering is needed of every online clustering step , but the speed of Dynamical K-means algorithm is accelerated . Simulation results on standard and artificial social network datasets demonstrate that comparing with classical k-means clustering means , the excellent clustering results can be obtained by this method and the concept drifting phenomenon can be monitored efficiently .
The goal of the work is to create a new approach to solving the tasks of clustering in data streams when information is fed observation-by-observation in online mode . The following tasks were solved in the article : the model of neural network ensembles for data clustering was created , the methods of data clustering to process mass data were developed , the methods of online data clustering of data using neural network ensembles working in the parallel mode were developed . The following results were obtained : the operation principles of the ensembles of the Kohonen neural network were formulated and practical requirements for dealing with mass data were specified .
This paper proposes exploiting static and dynamically mined metadata for enriching web searching with exploration services . Online results clustering , which is a mining task of dynamic nature since it is based on query-dependent snippets , is useful for providing users with overviews of the top results and thus allowing them to restrict their focus to the desired parts . On the other hand , the various static metadata that are available to a search engine ( e.g. , domain , language , date , and filetype ) are commonly exploited only through the advanced ( form-based ) search facilities that some WSEs offer ( and users rarely use ) .
Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task . Moreover , we also conduct computational complexity analysis and the result shows that MMRSC can effective handle the multi-view clustering problem without increasing the computational cost as compared to GraphSC .
Existing algorithms do not meet the requirements of multi-view image clustering under big data conditions . Here , we design a multi-view clustering algorithm for massive and unstructured images .
Experiments are performed using multiple datasets . The results show that the proposed algorithm is capable of multi-view clustering of image data with high accuracy , low error rate and favorable stability .
In this paper , we address the multi-view subspace clustering problem . Our method utilize the circulant algebra for tensor , which is constructed by stacking the subspace representation matrices of different views and then shifting , to explore the high order correlations underlying multi-view data .
In this paper , we address the multi-view subspace clustering problem . Our method utilizes the circulant algebra for tensor , which is constructed by stacking the subspace representation matrices of different views and then rotating , to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved , as well as the high order correlations underlying multi-view data can be explored .
With the ability to exploit the internal structure of data , graph-based models have received a lot of attention and have achieved great success in multiview subspace clustering for multimedia data . Most of the existing methods individually construct an affinity graph for each single view and fuse the result obtained from each single graph .
In this paper , we explore the problem of multiview subspace clustering . We introduce a low-rank tensor constraint to explore the complementary information from multiple views and , accordingly , establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering ( LT-MSC ) .
Abstract Multi-view subspace clustering is essential to many scientific problems . However , most existing methods suffer from three aspects of issues .
In recent years many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed . In this paper , we propose a model for multiple view unsupervised clustering based on Kohonen self-organizing-map algorithm . The results of simulations in two dimensional space using three views of training sets having different statistical properties have been presented .
Many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed . In this paper we have proposed a model for multiple view unsupervised clustering based on Kohonen self-organizing-map method .
Learning from multi-view data is important in many applications . In this paper , we propose a novel convex subspace representation learning method for unsupervised multi-view clustering . We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm .
Self-representation model has made good progress for a single view subspace clustering . This paper proposed the multi-view subspace clustering model based on self-representation .
The idea is to learn the model iteratively from easy to complex samples in a self - paced fashion . By virtue of its generality , the SPL theory has been widely applied to various tasks , such as multi - view clustering [ reference ] , multi - label propagation [ reference ] , multimedia event detection , and co - saliency detection . More recently , introduced the pre - defined learning curriculum to the conventional self - paced learning regime which can take into account both the helpful prior knowledge known before training and the self - learning progress during training .
Our model , called Comraf * ( pronounced Comraf-Star ) , efficiently incorporates various views in multi-modal clustering , by which it allows great modeling flexibility . Comraf
Finally , ( c ) a two-phase block-incremental tensor decomposition technique , BICP , efficiently and effectively maintains tensor decomposition results in the presence of incrementally evolving tensor data . We also present experimental results , with diverse data sets , that show that , if properly constructed , sub-tensor impact graphs can indeed help overcome various density and noise challenges in clustering of multi-modal data sets .
Abstract Multi-modal canonical correlation analysis ( MCCA ) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data . MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data .
Identifying possible threat actors from samples of malware remains an active area of research with important ramifications for cybersecruity practitioners . The unsupervised identification and characterization of malware samples has been primarily treated as an early integration , multi-modal clustering problem where all possible features derived from the samples are concatenated into one feature vector , which can then be fed into a standard unsupervised learning algorithm . In this work , we focus on characterizing malware samples into possible threat actors from both late integration and intermediate integration multi-modal data perspectives .
However , previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data , typically in multi-modal contexts . In this paper , we propose a new technique to accelerate the calculation of shared neighbours and we introduce a new multi-source shared neighbours scheme applied to multi-modal image clustering . We first extend existing SNN-based similarity measures to the case of multiple sources and we introduce an original automatic source selection step when building candidate clusters .
Generalizations to multiple types are non-trivial , plagued by challenges such as choice of types and numbers of models , sampling imbalance and parameter tuning . In this work , we formulate the multi-type subspace clustering problem as one of learning non-linear subspace filters via deep multi-layer perceptrons ( mlps ) . The response to the learnt subspace filters serve as the feature embedding that is clustering-friendly , i.e. , points of the same clusters will be embedded closer together through the network .
Moreover , most subspace multi-clustering methods are especially scalable for high-dimensional data , which has become more and more popular in real applications due to the advances of big data technologies . In this paper , we focus on the subject of subspace multi-clustering , which has not been reviewed by any previous survey . We formulate the subspace multi-clustering problem and categorize the methodologies in different perspectives ( e.g. , de-coupled methods and coupled methods ) . We compare different methods on a series of specific properties ( e.g. , input parameters and different kinds of subspaces ) and analyze the advantages and disadvantages .
We present convolutional neural network based approaches for unsupervised multimodal subspace clustering . The proposed framework consists of three main stages — multimodal encoder , self-expressive layer , and multimodal decoder .
Structure Preserving Large Imagery Reconstruction Image Similarity Measurement using Region Props, Color and Texture: An Approach An improved form of the ant lion optimization algorithm for image clustering problems Seismic facies recognition based on prestack data using two-dimensional convolutional auto-encoder and cluster analysis Towards an approach using metric learning for interactive semi-supervised clustering of images Commonality Preserving Image-Set Clustering Based on Diverse Density Commonality Preserving Multiple Instance Clustering Based on Diverse Density Cluster centres determination based on KD tree in K-Means clustering for area change detection Image Clustering : Comparison Of Two Color Segmentation Techniques Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis Heterogeneous Transfer Learning for Image Clustering via the SocialWeb Hierarchical sparse coding based on spatial pooling and multi-feature fusion Invariant Information Clustering for Unsupervised Image Classification and Segmentation A video face clustering approach based on sparse subspace representation Video Based Face Detection Process Video Based Face Recognition System A novel and robust face clustering method via adaptive difference dictionary Subspace Clustering Generalized projection based M-estimator: Theory and applications Learning a subspace for face image clustering via trace ratio criterion A Mixed Modified LBP Approach for the Recognition of Real Life Human Faces Linkage Based Face Clustering via Graph Convolution Network An online weight-based clustering algorithm for faces selection Face abstracting method and video abstracting method based on face recognition and devices thereof FaceNet: A unified embedding for face recognition and clustering Online learning-based clustering approach for news recommendation systems An incremental data-stream sketch using sparse random projections Dynamic cluster formation using populations of spiking neurons Partial distortion entropy maximization for online data clustering Robust clustering analysis for the management of self-monitoring distributed systems Online Spectral Clustering on Network Streams An Offline–Online Visual Framework for Clustering Memes in Social Media Research and Realization of a Web Information Extraction and Knowledge Presentation System KERNEL FUZZY CLUSTERING OF DATA STREAMS BASED ON THE ENSEMBLE OF NEURAL NETWORKS On exploiting static and dynamically mined metadata for exploratory web searching Multi-view image clustering based on sparse coding and manifold consensus An Improved Clustering Method for Multi-view Images Multi-View Subspace Clustering via Relaxed L1-Norm of Tensor Multi-Rank On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization Learning a Joint Affinity Graph for Multiview Subspace Clustering Low-Rank Tensor Constrained Multiview Subspace Clustering One-step Kernel Multi-view Subspace Clustering An Application of the Self-Organizing Map to Multiple View Unsupervised Learning Improvement of the Multiple-View Learning Based on the Self-Organizing Maps Convex Subspace Representation Learning from Multi-View Data Multi-View Subspace Clustering With Block Diagonal Representation Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Multi-modal Clustering for Multimedia Collections Effective Tensor-Based Data Clustering Through Sub-Tensor Impact Graphs Clustering Adaptive Canonical Correlations for High-dimensional Multi-modal Data Analysis of Malware Communities Using Multi-Modal Features Multi-source shared nearest neighbours for multi-modal image clustering Learning for Multi-Type Subspace Clustering Subspace multi-clustering: a review Deep Multimodal Subspace Clustering Networks
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KSE ISVC (1) ACCV Workshops (3) ACL/IJCNLP ICME 2019 IEEE/CVF International Conference on Computer Vision (ICCV) ICME Workshops CVPR 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) APNOMS IJCNN ICIG (3) ICCV ICAISC (2) ICAISC AAAI IJCAI CVPR
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