TY - GEN
T1 - DIMC-net
T2 - 28th ACM International Conference on Multimedia, MM 2020
AU - Wen, Jie
AU - Zhang, Zheng
AU - Zhang, Zhao
AU - Wu, Zhihao
AU - Fei, Lunke
AU - Xu, Yong
AU - Zhang, Bob
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/12
Y1 - 2020/10/12
N2 - In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is proposed to address the challenge of multi-view clustering on missing views. In particular, DIMC-net designs several view-specific encoders to extract the high-level information of multiple views and introduces a fusion graph based constraint to explore the local geometric information of data. To reduce the negative influence of missing views, a weighted fusion layer is introduced to obtain the consensus representation shared by all views. Moreover, a clustering layer is introduced to guarantee that the obtained consensus representation is the best one for the clustering task. Compared with the existing deep learning based approaches, DIMC-net is more flexible and efficient since it can handle all kinds of incomplete cases and directly produce the clustering results. Experimental results show that DIMC-net achieves significant improvement over state-of-the-art incomplete multi-view clustering methods.
AB - In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is proposed to address the challenge of multi-view clustering on missing views. In particular, DIMC-net designs several view-specific encoders to extract the high-level information of multiple views and introduces a fusion graph based constraint to explore the local geometric information of data. To reduce the negative influence of missing views, a weighted fusion layer is introduced to obtain the consensus representation shared by all views. Moreover, a clustering layer is introduced to guarantee that the obtained consensus representation is the best one for the clustering task. Compared with the existing deep learning based approaches, DIMC-net is more flexible and efficient since it can handle all kinds of incomplete cases and directly produce the clustering results. Experimental results show that DIMC-net achieves significant improvement over state-of-the-art incomplete multi-view clustering methods.
KW - deep multi-view clustering
KW - incomplete multi-view clustering
KW - view-specific encoders
KW - weighted fusion
UR - https://www.scopus.com/pages/publications/85106122516
U2 - 10.1145/3394171.3413807
DO - 10.1145/3394171.3413807
M3 - 会议稿件
AN - SCOPUS:85106122516
T3 - MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
SP - 3753
EP - 3761
BT - MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
Y2 - 12 October 2020 through 16 October 2020
ER -