Deep Embedded Multi-view Clustering with Collaborative Training

Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.

[1]  En Zhu,et al.  Deep Clustering with Convolutional Autoencoders , 2017, ICONIP.

[2]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[3]  Guoyin Wang,et al.  An active three-way clustering method via low-rank matrices for multi-view data , 2020, Inf. Sci..

[4]  Jiancheng Lv,et al.  COMIC: Multi-view Clustering Without Parameter Selection , 2019, ICML.

[5]  Zenglin Xu,et al.  Auto-weighted multi-view co-clustering with bipartite graphs , 2020, Inf. Sci..

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

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

[8]  Vishal M. Patel,et al.  Deep Multimodal Subspace Clustering Networks , 2018, IEEE Journal of Selected Topics in Signal Processing.

[9]  Yuan Xie,et al.  Jointly Deep Multi-View Learning for Clustering Analysis , 2018 .

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

[11]  Zenglin Xu,et al.  Semi-supervised deep embedded clustering , 2019, Neurocomputing.

[12]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[13]  Dacheng Tao,et al.  Multi-view Self-Paced Learning for Clustering , 2015, IJCAI.

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

[15]  Hongdong Li,et al.  Neural Collaborative Subspace Clustering , 2019, ICML.

[16]  Riadh Ksantini,et al.  Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift , 2019, IEEE Transactions on Knowledge and Data Engineering.

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

[18]  Mohammad Rahmati,et al.  Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data , 2019, Inf. Sci..

[19]  Lingfeng Wang,et al.  Deep Adaptive Image Clustering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[20]  Zenglin Xu,et al.  Deep Density-based Image Clustering , 2018, Knowl. Based Syst..

[21]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[23]  Yuan Xie,et al.  Deep Multi-View Clustering via Multiple Embedding , 2018, ArXiv.

[24]  Ali Farhadi,et al.  Unsupervised Deep Embedding for Clustering Analysis , 2015, ICML.

[25]  Bo Yang,et al.  Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering , 2016, ICML.

[26]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[27]  Ling Shao,et al.  Binary Multi-View Clustering , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  En Zhu,et al.  Deep Embedded Clustering with Data Augmentation , 2018, ACML.

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

[30]  Tong Zhang,et al.  Deep Subspace Clustering Networks , 2017, NIPS.

[31]  Jianping Yin,et al.  Improved Deep Embedded Clustering with Local Structure Preservation , 2017, IJCAI.

[32]  Xuan Tang,et al.  Deep Multi-view Sparse Subspace Clustering , 2018, ICNCC.

[33]  Jeff A. Bilmes,et al.  On Deep Multi-View Representation Learning , 2015, ICML.

[34]  Ian D. Reid,et al.  Scalable Deep k-Subspace Clustering , 2018, ACCV.

[35]  Ivica Kopriva,et al.  Multi-view low-rank sparse subspace clustering , 2017, Pattern Recognit..

[36]  Guo Zhong,et al.  Nonnegative self-representation with a fixed rank constraint for subspace clustering , 2020, Inf. Sci..

[37]  Cheng Deng,et al.  Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[38]  Roland Vollgraf,et al.  Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.

[39]  Zenglin Xu,et al.  Self-paced and auto-weighted multi-view clustering , 2020, Neurocomputing.