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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Vydáno v:	Neurocomputing (Amsterdam) Ročník 364; s. 129 - 137
Hlavní autoři:	Wang, Peitao, He, Zhaoshui, Xie, Kan, Gao, Junbin, Antolovich, Michael, Tan, Beihai
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 28.10.2019
Témata:	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
On-line přístup:	Získat plný text
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