Multi-View Clustering With Hybrid-Order Similarity Learning.

Abstract

Multi-view clustering (MVC) has attracted significant attention in recent years due to its ability to leverage heterogeneous features from multiple views. However, existing methods often lack the ability to jointly model first-order and topological relationships, which is crucial for capturing a more comprehensive clustering structure. In this article, we propose a novel multi-view clustering method with hybrid-order similarity learning (MCHL), which integrates multiple view-specific graphs while considering their first-order and topological correlations, and iteratively learns the view weights and the consensus graph within a unified framework. In addition, we impose a connectivity constraint on the consensus graph to ensure that data points belonging to the same cluster are properly connected within the same component. Extensive experiments on multiple benchmark datasets demonstrate the superior clustering performance of MCHL over the state-of-the-art methods.