Individuality Meets Commonality: A Unified Graph Learning Framework for Multi-View Clustering

Abstract

Multi-view clustering, which aims at boosting the clustering performance by leveraging the individual information and the common information of multi-view data, has gained extensive consideration in recent years. However, most existing multi-view clustering algorithms either focus on extracting the multi-view individuality or emphasize on exploring the multi-view commonality, neither of which can fully utilize the comprehensive information from multiple views. To this end, we propose a novel algorithm named V iew-specific and C onsensus G raph A lignment (VCGA) for multi-view clustering, which simultaneously formulates the multi-view individuality and the multi-view commonality into a unified framework to effectively partition data points. To be specific, the VCGA model constructs the view-specific graphs and the shared graph from original multi-view data and hidden latent representation, respectively. Furthermore, the view-specific graphs of different views and the consensus graph are aligned into an informative target graph, which is employed as a crucial input to the standard spectral clustering method for clustering. Extensive experimental results on six benchmark datasets demonstrate the superiority of our method against other state-of-the-art clustering algorithms.