计算机科学
判别式
聚类分析
图形
人工智能
特征学习
机器学习
数据挖掘
模式识别(心理学)
理论计算机科学
作者
Cheng Liu,Si Wu,Rui Li,Dazhi Jiang,Hau-San Wong
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-09-01
卷期号:35 (9): 9394-9406
被引量:15
标识
DOI:10.1109/tkde.2023.3238416
摘要
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.
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