Enhanced non-negative matrix factorization via adaptive weighted bipartite graph learning for clustering problems

Non-negative matrix factorization (NMF)-based clustering models, widely employed in modern applications, typically consist of two principal stages: obtaining low-dimensional representation through NMF and applying clustering algorithms such as k-means to the representation. However, traditional NMF...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 650; S. 130871
Hauptverfasser:	Huang, Yulei, Liu, Libo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 14.10.2025
Schlagworte:	Basis vectors Bipartite graph Cluster centroids Non-negative matrix factorization Bipartite graph Non-negative matrix factorization Basis vectors Cluster centroids
ISSN:	0925-2312
Online-Zugang:	Volltext
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