Multi-view spectral clustering algorithm based on bipartite graph and multi-feature similarity fusion.

Abstract

Multi-view clustering remains a challenging task due to the heterogeneity and inconsistency across multiple views. Most esisting multi-view spectral clustering methods adopt a two-stage approch-constructing fused spectral embeddings matrix followed by k-means clustering-which often leads to information loss and suboptimal performance. Moreover, current graph and feature fusion strategies struggle to address view-specific discrepancies and label misalignment, while their high computational complexity hinders scalability to large datasets. To overcome these limitations, we propose a unified Multi-view Spectral Clustering algorithm based on Bipartite Graph and Multi-feature Similarity Fusion (BG-MFS). The proposed framework jointly integrates bipartite graph construction, multi-feature similarity fusion, and discrete clustering within a single optimization model, enabling mutual reinforcement among components. Furthermore, an entropy-based weighting mechanism is introduced to adaptively assess the contribution of each view. Extensive experiments demonstrate that BG-MFS consistently outperforms state-of-the-art methods in both clustering accuracy and computational efficiency.