.png){kind=icon}
返回
Self-Supervised Graph Completion for Incomplete Multi-View Clustering
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2023.3238416.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Incomplete multi-view clustering (IMVC) is challenging, as it requires adequately exploring complementary and consistency information under the incompleteness of data. Most existing approaches attempt to overcome the incompleteness at instance-level. In this work, we develop a new approach to facilitate IMVC from a new perspective. Specifically, we transfer the issue of missing instances to a similarity graph completion problem for incomplete views, and propose a self-supervised multi-view graph completion algorithm to infer the associated missing entries. Further, by incorporating constrained feature learning, the inferred graph can be naturally leveraged in representation learning. We theoretically show that our feature learning process performs an Auto-Regressive filter function by encoding the learned similarity graph, which could yield discriminative representation for a clustering task. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods.
Keyword:
Index Terms-Incomplete multi-view clustering
self-supervised graph completion
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
机构
引用论文
The changing epidemiology of paediatric brain tumours: a review from the Hospital for Sick Children
PREAI
分享收藏The Road to Rio: Medical and Scientific Perspectives on the 2016 Paralympic Games里约之路: 2016残奥会的医学和科学观点.png)
PM&R
OAAI
PM&RIF0
OAAI分享收藏
查看更多内容