  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.
