Binary sparse nonnegative matrix factorization

Yuan, Y; Li, X; Pang, Y; Lu, X; Tao, D

Binary sparse nonnegative matrix factorization

Yuan, Y Li, X Pang, Y Lu, X Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19 (5), pp. 772 - 777
Issue Date:: 2009-05-01

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Field	Value	Language
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Pang, Y	en_US
dc.contributor.author	Lu, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-05-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19 (5), pp. 772 - 777	en_US
dc.identifier.issn	1051-8215	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15145
dc.description.abstract	This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are much faster than those of binary principal component analysis (B-PCA). Besides, B-SNMF is more robust to occlusions in images. Experimental results on face images demonstrate the effectiveness and the efficiency of the proposed B-SNMF. © 2009 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology	en_US
dc.relation.isbasedon	10.1109/TCSVT.2009.2017306	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Binary sparse nonnegative matrix factorization	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	19	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are much faster than those of binary principal component analysis (B-PCA). Besides, B-SNMF is more robust to occlusions in images. Experimental results on face images demonstrate the effectiveness and the efficiency of the proposed B-SNMF. © 2009 IEEE.

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http://hdl.handle.net/10453/15145