Enhancing Inter-Class Separability With High-Order Strangers for Multi-View Clustering

Abstract

Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.