Contrastive and attentive graph learning for multi-view clustering

作者：

Highlights：

• A novel CAGL framework for multi-view clustering is proposed.

• CL is used to achieve fine-modeling and ensure the consistency of multiple views.

• An Att-weighted module to capture the difference of each view in multiple views.

• The performance of our method outperforms the popular baselines on six benchmarks.

摘要

•A novel CAGL framework for multi-view clustering is proposed.•CL is used to achieve fine-modeling and ensure the consistency of multiple views.•An Att-weighted module to capture the difference of each view in multiple views.•The performance of our method outperforms the popular baselines on six benchmarks.

论文关键词：Graph learning,Contrastive learning,Attention networks,Multi-view clustering

论文评审过程：Received 1 November 2021, Revised 15 April 2022, Accepted 27 April 2022, Available online 10 May 2022, Version of Record 10 May 2022.

论文官网地址：https://doi.org/10.1016/j.ipm.2022.102967