Multi-View Clustering Meets Heterogenous Data: A Fusion Regularized Method

Abstract

Multi-view clustering leverages consistent and complementary information across multiple views to provide more comprehensive insights than single-view analysis. However, the heterogeneity and redundancy of multi-view data pose significant challenges to the existing clustering techniques. To tackle these challenges effectively, this paper proposes a novel multi-view fusion regularized clustering method with adaptive group sparsity, enabling discriminative clustering while capturing informative features. Technically, for heterogeneous multi-view data with mixed-type feature sets, different losses or divergence metrics are considered with a joint fusion penalty to obtain consistent cluster structures. Moreover, the non-convex group sparsity consisting of inter-group sparsity and intra-group sparsity is utilized to eliminate redundant features, thereby enhancing the robustness. Furthermore, we develop an effective alternating direction method of multipliers (ADMM), where all subproblems can be solved in closed form. Extensive numerical experiments on real data validate the superior performance of our presented method in clustering accuracy and feature selection.