Abstract
Multi-view subspace clustering has become a hot unsupervised learning task, since it could fuse complementary multi-view information from multiple data effectively. However, most existing methods either fail to incorporate the clustering process into the feature learning process, or cannot integrate multi-view relationships well into the data reconstruction process, which thus damages the final clustering performance. To overcome the above shortcomings, we propose the deep contrastive multi-view subspace clustering method (DCMSC), which is the first attempt to integrate the contrastive learning into deep multi-view subspace clustering. Specifically, DCMSC includes multiple autoencoders for self-expression learning to learn self-representation matrices for multiple views which would be fused into one unified self-representation matrix to effectively utilize the consistency and complementarity of multiple views. Meanwhile, to further exploit multi-view relations, DCMSC also introduces contrastive learning into multi-autoencoder network and Hilbert Schmidt Independence Criterion (HSIC) to better exploit complementarity. Extensive experiments on several real-world multi-view datasets demonstrate the effectiveness of our proposed method by comparing with state-of-the-art multi-view clustering methods.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 62071142 and 62106063, by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011406, by the Shenzhen College Stability Support Plan under Grants GXWD20201230155427003-20200824210638001 and GXWD20201230155427003-20200824113231001, by the Guangdong Natural Science Foundation under Grant 2022A1515010819, and by Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005.
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Cheng, L., Chen, Y., Hua, Z. (2023). Deep Contrastive Multi-view Subspace Clustering. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_58
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DOI: https://doi.org/10.1007/978-981-99-1639-9_58
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