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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Veröffentlicht in:	Neural networks Jg. 194; S. 108177
Hauptverfasser:	Li, Shunyong, Liu, Kun, Zheng, Mengjiao, Bai, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.02.2026
Schlagworte:	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-Zugang:	Volltext
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