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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Bibliographic Details
Published in:Neural networks Vol. 194; p. 108177
Main Authors: Li, Shunyong, Liu, Kun, Zheng, Mengjiao, Bai, Liang
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.02.2026
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2025.108177