Self-Supervised Graph Completion for Incomplete Multi-View Clustering

Cheng Liu

doi:10.1109/TKDE.2023.3238416

Self-Supervised Graph Completion for Incomplete Multi-View Clustering

2023-09-01

37

PRE

AI

DOI：10.1109/TKDE.2023.3238416

Original

Original request for help

Save

Abstract

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.

Keywords:

Index Terms-Incomplete multi-view clustering

self-supervised graph completion

Self-Supervised Graph Completion for Incomplete Multi-View Clustering

Abstract

Journal

Organization