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

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 informat...

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Vydáno v:	Neural networks Ročník 194; s. 108177
Hlavní autoři:	Li, Shunyong, Liu, Kun, Zheng, Mengjiao, Bai, Liang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.02.2026
Témata:	Bipartite graph Clustering indicator matrix Multi-feature similarity fusion Clustering indicator matrix Bipartite graph Multi-feature similarity fusion
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2025.108177