Deep Multiple Auto-Encoder-Based Multi-view Clustering

Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.

[1]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[2]  Ming Li,et al.  Feature extraction via multi-view non-negative matrix factorization with local graph regularization , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[3]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[4]  Chang Tang,et al.  Diversity and consistency learning guided spectral embedding for multi-view clustering , 2019, Neurocomputing.

[5]  Kun Zhan,et al.  Graph Learning for Multiview Clustering , 2018, IEEE Transactions on Cybernetics.

[6]  Shengli Xie,et al.  Deep graph regularized non-negative matrix factorization for multi-view clustering , 2020, Neurocomputing.

[7]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[8]  Zenglin Xu,et al.  Auto-weighted multi-view clustering via deep matrix decomposition , 2020, Pattern Recognit..

[9]  Xiaochun Cao,et al.  Diversity-induced Multi-view Subspace Clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[12]  Stan Z. Li,et al.  Exclusivity-Consistency Regularized Multi-view Subspace Clustering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

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

[15]  Xin Liu,et al.  Document clustering based on non-negative matrix factorization , 2003, SIGIR.

[16]  Johan A. K. Suykens,et al.  Multi-View Kernel Spectral Clustering , 2018, Inf. Fusion.

[17]  Hamido Fujita,et al.  A study of graph-based system for multi-view clustering , 2019, Knowl. Based Syst..

[18]  Zenglin Xu,et al.  Auto-weighted multi-view clustering via kernelized graph learning , 2019, Pattern Recognit..

[19]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[20]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[21]  Cai Xu,et al.  Deep Multi-View Concept Learning , 2018, IJCAI.

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

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

[24]  Jeff A. Bilmes,et al.  Deep Canonical Correlation Analysis , 2013, ICML.

[25]  Haibo Wang,et al.  Adaptive Structure Concept Factorization for Multiview Clustering , 2018, Neural Computation.

[26]  Wei Zhang,et al.  Consistent and Specific Multi-View Subspace Clustering , 2018, AAAI.

[27]  Sham M. Kakade,et al.  Multi-view clustering via canonical correlation analysis , 2009, ICML '09.

[28]  Lizhen Wang,et al.  Multi-view Clustering via Multiple Auto-Encoder , 2020, APWeb/WAIM.

[29]  Thomas S. Huang,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation. , 2011, IEEE transactions on pattern analysis and machine intelligence.

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

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

[32]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .