Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding

• URL: http://arxiv.org/abs/2312.08697v1
• Date: Thu, 14 Dec 2023 07:28:41 GMT
• Title: Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding
• Authors: Guoqing Chao, Yi Jiang, Dianhui Chu
• Abstract summary: We propose a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC) Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information.
• Score: 7.305817202715752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete multi-view clustering becomes an important research problem, since multi-view data with missing values are ubiquitous in real-world applications. Although great efforts have been made for incomplete multi-view clustering, there are still some challenges: 1) most existing methods didn't make full use of multi-view information to deal with missing values; 2) most methods just employ the consistent information within multi-view data but ignore the complementary information; 3) For the existing incomplete multi-view clustering methods, incomplete multi-view representation learning and clustering are treated as independent processes, which leads to performance gap. In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information while instance-level contrastive learning for latent representation is designed to employ the consistent information. Thirdly, an end-to-end framework is proposed to integrate multi-view missing values handling, multi-view representation learning and clustering assignment for joint optimization. Experiments compared with state-of-the-art approaches demonstrated the effectiveness and superiority of our method. Our code is publicly available at https://github.com/liunian-Jay/ICMVC.

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