GMC: Graph-Based Multi-View Clustering

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly.

[1]  Liang Wang,et al.  Unified subspace learning for incomplete and unlabeled multi-view data , 2017, Pattern Recognit..

[2]  Changyin Sun,et al.  Discriminative Multi-View Interactive Image Re-Ranking , 2017, IEEE Transactions on Image Processing.

[3]  Xuelong Li,et al.  Multi-view Subspace Clustering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Feiping Nie,et al.  The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.

[5]  K. Fan On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations I. , 1949, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Xuelong Li,et al.  Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification , 2016, IJCAI.

[7]  TaoDacheng,et al.  Large-Margin Multi-ViewInformation Bottleneck , 2014 .

[8]  Aristidis Likas,et al.  Kernel-Based Weighted Multi-view Clustering , 2012, 2012 IEEE 12th International Conference on Data Mining.

[9]  Xuelong Li,et al.  Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification , 2018, IEEE Transactions on Image Processing.

[10]  Hong Yu,et al.  Multi-view clustering via multi-manifold regularized non-negative matrix factorization , 2017, Neural Networks.

[11]  Lin Wu,et al.  Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus , 2015, IEEE Transactions on Image Processing.

[12]  Dacheng Tao,et al.  Large-Margin Multi-ViewInformation Bottleneck , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Xuelong Li,et al.  Self-weighted Multiview Clustering with Multiple Graphs , 2017, IJCAI.

[14]  Feiping Nie,et al.  Large-Scale Multi-View Spectral Clustering via Bipartite Graph , 2015, AAAI.

[15]  Shiliang Sun,et al.  Multi-view learning overview: Recent progress and new challenges , 2017, Inf. Fusion.

[16]  Roberto Tagliaferri,et al.  Impact of different metrics on multi-view clustering , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[17]  Mehryar Mohri,et al.  Sample Selection Bias Correction Theory , 2008, ALT.

[18]  Shenglan Liu,et al.  Multi-view spectral clustering via robust local subspace learning , 2017, Soft Comput..

[19]  Moumita Saha,et al.  A Graph Based Approach to Multiview Clustering , 2013, PReMI.

[20]  K. Fan On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations: II. , 1949, Proceedings of the National Academy of Sciences of the United States of America.

[21]  B. Mohar THE LAPLACIAN SPECTRUM OF GRAPHS y , 1991 .

[22]  Yang Wang,et al.  Towards metric fusion on multi-view data: a cross-view based graph random walk approach , 2013, CIKM.

[23]  Xiaojun Chang,et al.  Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features , 2019, IEEE Transactions on Cybernetics.

[24]  Qinghua Hu,et al.  Latent Multi-view Subspace Clustering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[26]  Syed Fawad Hussain,et al.  Co-clustering of multi-view datasets , 2015, Knowledge and Information Systems.

[27]  Jing-Yu Yang,et al.  Multiple kernel clustering based on centered kernel alignment , 2014, Pattern Recognit..

[28]  Shiliang Sun,et al.  A Survey on Multiview Clustering , 2017, IEEE Transactions on Artificial Intelligence.

[29]  Yong Luo,et al.  Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Feiping Nie,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Multi-View K-Means Clustering on Big Data , 2022 .

[32]  Lin Wu,et al.  Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering , 2017, Neural Networks.

[33]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.

[34]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Jinbo Bi,et al.  Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization , 2015, ICML.

[36]  Dimitri P. Bertsekas,et al.  Constrained Optimization and Lagrange Multiplier Methods , 1982 .

[37]  Michael William Newman,et al.  The Laplacian spectrum of graphs , 2001 .

[38]  Dacheng Tao,et al.  Multi-View Intact Space Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Dacheng Tao,et al.  A Survey on Multi-view Learning , 2013, ArXiv.

[40]  Quanzeng You,et al.  Clusterability Analysis and Incremental Sampling for Nyström Extension Based Spectral Clustering , 2011, 2011 IEEE 11th International Conference on Data Mining.

[41]  Lin Wu,et al.  Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Lin Wu,et al.  Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering , 2016, IJCAI.

[43]  Yongdong Zhang,et al.  Multiview Spectral Embedding , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[44]  Donato Malerba,et al.  A Co-Training Strategy for Multiple View Clustering in Process Mining , 2016, IEEE Transactions on Services Computing.

[45]  Dong Yue,et al.  Multi-view low-rank dictionary learning for image classification , 2016, Pattern Recognit..

[46]  Lei Du,et al.  Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition , 2014, AAAI.

[47]  Yong Dou,et al.  Multiple Kernel Clustering Framework with Improved Kernels , 2017, IJCAI.

[48]  Dacheng Tao,et al.  Multi-View Learning With Incomplete Views , 2015, IEEE Transactions on Image Processing.

[49]  Jianping Fan,et al.  Multi-View Concept Learning for Data Representation , 2015, IEEE Transactions on Knowledge and Data Engineering.

[50]  Hao Wang,et al.  Multi-view clustering: A survey , 2018, Big Data Min. Anal..

[51]  Yun Fu,et al.  Multi-View Clustering via Deep Matrix Factorization , 2017, AAAI.

[52]  Feiping Nie,et al.  Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight , 2017, IEEE Transactions on Knowledge and Data Engineering.

[53]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[54]  Shuicheng Yan,et al.  Convex Sparse Spectral Clustering: Single-View to Multi-View , 2015, IEEE Transactions on Image Processing.

[55]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[56]  Wei Yuan,et al.  Multi-view manifold learning with locality alignment , 2018, Pattern Recognit..

[57]  Jiawei Han,et al.  Multi-View Clustering via Joint Nonnegative Matrix Factorization , 2013, SDM.

[58]  Feiping Nie,et al.  Unsupervised Single and Multiple Views Feature Extraction with Structured Graph , 2017, IEEE Transactions on Knowledge and Data Engineering.

[59]  Shiliang Sun,et al.  PAC-Bayes analysis of multi-view learning , 2014, Inf. Fusion.