Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering

TL;DR

This paper introduces CI-GMVC, a novel graph-based multi-view clustering method that explicitly models and leverages both consistent and inconsistent information across multiple views, improving clustering robustness.

Contribution

It proposes a new GMVC approach that explicitly incorporates consistent and inconsistent parts of multi-view data, addressing limitations of existing methods.

Findings

Demonstrates improved clustering performance on real-world datasets.

Effectively handles inconsistencies across multiple views.

Outperforms existing GMVC methods in experiments.

Abstract

Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC.