Multi-view Clustering based on Doubly Stochastic Graph

Abstract

Most Multi-view Graph-based Clustering (MGC) models always obtain suboptimal performance since the necessary symmetry of graph is ignored during the process of graph fusion. To solve the problem, we propose Multi-view Clustering based on Doubly Stochastic Graph (MCDSG). Our MCDSG precalculates Single-view Similarity Graphs (SSGs) and then fuses them into a consensus one with doubly stochastic (non-negative, sum-to-one and symmetry) constraints, directly providing clustering results by its connectivity. For optimization, a novel and easy-understanding Augmented Lagrangian Method (ALM) is proposed to substitute the widely used Von-Neumann Successive Projection (VNSP) method, which simultaneously optimizes all the doubly stochastic conditions to the optimal solution. To verify the robustness to noisy data sets, we propose a pipeline to add noise to the key features of face images and obtain a two-view data set termed NoisedORL. Experiments on both synthetic data sets and real benchmarks show that our MCDSG achieves SOTA clustering performance against nine methods. Code will be published at https://github.com/NianWang-HJJGCDX/MCDSG.