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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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 275; pp. 1012 - 1020
Main Authors:	Chen, Baiyu, Yang, Zi, Yang, Zhouwang
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 31.01.2018
Subjects:	Augmented Lagrangian method Low-rank representation Matrix factorization Sparseness Augmented Lagrangian method Matrix factorization Low-rank representation Sparseness
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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