TY - GEN
T1 - Structural Deep Incomplete Multi-view Clustering Network
AU - Wen, Jie
AU - Wu, Zhihao
AU - Zhang, Zheng
AU - Fei, Lunke
AU - Zhang, Bob
AU - Xu, Yong
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/10/30
Y1 - 2021/10/30
N2 - In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.
AB - In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.
KW - deep multi-view clustering
KW - graph convolutional network
KW - incomplete multi-view clustering
KW - view-specific encoders
UR - https://www.scopus.com/pages/publications/85119197840
U2 - 10.1145/3459637.3482192
DO - 10.1145/3459637.3482192
M3 - 会议稿件
AN - SCOPUS:85119197840
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3538
EP - 3542
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Y2 - 1 November 2021 through 5 November 2021
ER -