Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering
This work aims to improve multi-view clustering performance for machine learning applications by handling inconsistencies in multi-view data, which is an incremental improvement to existing GMVC methods.
This paper proposes a new graph-based multi-view clustering method, CI-GMVC, that explicitly addresses both consistent and inconsistent parts of input graph matrices. Numerical evaluations on real-world datasets demonstrate its effectiveness.
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.