Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization

Guan, N; Wei, L; Luo, Z; Tao, D

Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization

Guan, N Wei, L Luo, Z Tao, D

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Publisher:: Public Library Science
Publication Type:: Journal Article
Citation:: Guan, Naiyang et al. 2013, 'Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization', Plos One, vol. 8, no. 10, pp. e77162-1-e77162-10.
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Guan, N	en_US
dc.contributor.author	Wei, L	en_US
dc.contributor.author	Luo, Z	en_US
dc.contributor.author	Tao, D	en_US
dc.date.accessioned	2014-04-14T02:53:47Z
dc.date.available	2014-04-14T02:53:47Z
dc.date.issued	2013	en_US
dc.identifier	2013001003	en_US
dc.identifier.citation	Guan, Naiyang et al. 2013, 'Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization', Plos One, vol. 8, no. 10, pp. e77162-1-e77162-10.	en_US
dc.identifier.issn	1932-6203	en_US
dc.identifier.other	C1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27149
dc.description.abstract	Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix X is an element of R-mxn to the product of two lower-rank nonnegative factor matrices, i.e., W is an element of R-mxr and H is an element of R-rxn (r<min{m,n})	en_US
dc.publisher	Public Library Science	en_US
dc.relation.ispartof	Plos One	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.isbasedon	http://dx.doi.org/10.1371/journal.pone.0077162	en_US
dc.subject	Recognition; Information; Framework; Parts	en_US
dc.subject.classification	Artificial Intelligence and Image Processing	en_US
dc.title	Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization	en_US
dc.type	Journal article
utslib.citation.volume	8	en_US
utslib.citation.number	10	en_US
utslib.location	San Francisco	en_US
utslib.citation.startpage	e77162-1	en_US
utslib.citation.endpage	e77162-10	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080100	en_US
utslib.percentage	100	en_US
utslib.copyright.status	open_access

Abstract:

Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix X is an element of R-mxn to the product of two lower-rank nonnegative factor matrices, i.e., W is an element of R-mxr and H is an element of R-rxn (r

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http://hdl.handle.net/10453/27149