An algorithm for low-rank matrix factorization and its applications

This paper proposes a valid and fast algorithm for low-rank matrix factorization. There are multiple applications for low-rank matrix factorization, and numerous algorithms have been developed to solve this problem. However, many algorithms do not use rank directly; instead, they minimize a nuclear...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 275; S. 1012 - 1020
Hauptverfasser:	Chen, Baiyu, Yang, Zi, Yang, Zhouwang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 31.01.2018
Schlagworte:	Augmented Lagrangian method Low-rank representation Matrix factorization Sparseness Augmented Lagrangian method Matrix factorization Low-rank representation Sparseness
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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