Convex matching pursuit for large-scale sparse coding and subset selection

Tan, M; Tsang, IW; Wang, L; Zhang, X

Convex matching pursuit for large-scale sparse coding and subset selection

Tan, M Tsang, IW

Wang, L Zhang, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2012, 2 pp. 1119 - 1125
Issue Date:: 2012-11-07

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Field	Value	Language
dc.contributor.author	Tan, M	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Zhang, X	en_US
dc.date.issued	2012-11-07	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2012, 2 pp. 1119 - 1125	en_US
dc.identifier.isbn	9781577355687	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30590
dc.description.abstract	In this paper, a new convex matching pursuit scheme is proposed for tackling large-scale sparse coding and subset selection problems. In contrast with current matching pursuit algorithms such as subspace pursuit (SP), the proposed algorithm has a convex formulation and guarantees that the objective value can be monotonically decreased. Moreover, theoretical analysis and experimental results show that the proposed method achieves better scalability while maintaining similar or better decoding ability compared with state-of-the-art methods on large-scale problems. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Convex matching pursuit for large-scale sparse coding and subset selection	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Toronto, Canada
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

In this paper, a new convex matching pursuit scheme is proposed for tackling large-scale sparse coding and subset selection problems. In contrast with current matching pursuit algorithms such as subspace pursuit (SP), the proposed algorithm has a convex formulation and guarantees that the objective value can be monotonically decreased. Moreover, theoretical analysis and experimental results show that the proposed method achieves better scalability while maintaining similar or better decoding ability compared with state-of-the-art methods on large-scale problems. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

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http://hdl.handle.net/10453/30590