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....

Celý popis

Podrobná bibliografia

Vydané v:	Neurocomputing (Amsterdam) Ročník 364; s. 129 - 137
Hlavní autori:	Wang, Peitao, He, Zhaoshui, Xie, Kan, Gao, Junbin, Antolovich, Michael, Tan, Beihai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 28.10.2019
Predmet:	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 prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!