TY - GEN
T1 - Deep Contrastive Multi-view Subspace Clustering
AU - Cheng, Lei
AU - Chen, Yongyong
AU - Hua, Zhongyun
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Contrastive learning
KW - Hilbert Schmidt Independence Criterion
KW - Multi-view Subspace clustering
UR - https://www.scopus.com/pages/publications/85161685599
U2 - 10.1007/978-981-99-1639-9_58
DO - 10.1007/978-981-99-1639-9_58
M3 - 会议稿件
AN - SCOPUS:85161685599
SN - 9789819916382
T3 - Communications in Computer and Information Science
SP - 692
EP - 704
BT - Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings
A2 - Tanveer, Mohammad
A2 - Agarwal, Sonali
A2 - Ozawa, Seiichi
A2 - Ekbal, Asif
A2 - Jatowt, Adam
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Neural Information Processing, ICONIP 2022
Y2 - 22 November 2022 through 26 November 2022
ER -