Robust multi-view discrete clustering with unified graph learning

Abstract

Graph-based multi-view clustering (GMVC) has garnered significant attention due to its ability to overcome sample space shape constraints. However, existing GMVC methods encounter two major challenges: (1) Their effectiveness diminishes because they solely rely on sample-constructed graphs and the two-stage mismatch caused by additional discretization; (2) Their robustness deteriorates substantially when applied to real-world datasets that contain complex noise. To address these limitations, we propose a robust multi-view discrete clustering model with unified graph learning (RCUGL). This model integrates richer graph structural information and accommodates complex noise clustering tasks. Specifically, we incorporated low-rank approximation graphs reconstructed from spectral embeddings and graphs constructed by samples into a unified graph to provide enriched structural insights. Subsequently, within the framework of the correntropy, discrete spectral analysis was performed directly on the unified graph to derive cluster assignments. Given the non-convex and discrete nature of the proposed RCUGL model, we developed a half-quadratic-based coordinate descent optimisation algorithm to ensure rapid and reliable convergence. Extensive experiments demonstrate that RCUGL substantially improves clustering effectiveness, comparable to state-of-the-art methods.