A hybrid algorithm for low-rank approximation of nonnegative matrix factorization

Nonnegative matrix factorization (NMF) is a recently developed method for data analysis. So far, most of known algorithms for NMF are based on alternating nonnegative least squares (ANLS) minimization of the squared Euclidean distance between the original data matrix and its low-rank approximation....

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 364; S. 129 - 137
Hauptverfasser:	Wang, Peitao, He, Zhaoshui, Xie, Kan, Gao, Junbin, Antolovich, Michael, Tan, Beihai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 28.10.2019
Schlagworte:	Alternately updating Low-rank approximation Nonnegative matrix factorization Nonnegative Tucker decomposition Alternately updating Nonnegative Tucker decomposition Low-rank approximation Nonnegative matrix factorization
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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