Multi-View Clustering Through Self-Weighted High-Order Similarity Fusion

Hong Peng, Hongmin Cai

Abstract: Recently, multi-view clustering methods based on high-order sample affinities to ease learning complex structures attract much attention. However, most of the methods used pre-defined similarity, which is easy to be corrupted by noises and yield suboptimal performance. To tackle with this issue, this paper proposes a novel multi-view clustering method, named by WHSF, which seeks to learn a self-weighted high-order similarity. The high-order similarity is formulated to flexibly capture the intrinsic structure of data, characterized by fusing the interactions across views. A high-order regularization based on the defined similarity is incorporated into the model and assigned with weight parameters, enabling the model to focus on mutual information among views. Extensive experiments on four real-world datasets show that the proposed WHSF outperforms benchmark multi-view methods and can reveal a reliable structure concealed across multiple views.