Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding

TL;DR

This paper introduces a novel multi-view clustering method that effectively handles missing data, exploits both consistent and complementary information, and jointly optimizes representation learning and clustering for improved performance.

Contribution

The proposed ICMVC method innovatively integrates multi-view consistency, complementary information, and joint optimization, addressing key challenges in incomplete multi-view clustering.

Findings

Outperforms state-of-the-art methods in experiments

Effectively handles missing multi-view data

Jointly optimizes representation learning and clustering

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.