Matching pursuit LASSO part II: Applications and sparse recovery over batch signals

Tan, M; Tsang, IW; Wang, L

Matching pursuit LASSO part II: Applications and sparse recovery over batch signals

Tan, M Tsang, IW

Wang, L

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Signal Processing, 2015, 63 (3), pp. 742 - 753
Issue Date:: 2015-02-01

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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.date.issued	2015-02-01	en_US
dc.identifier.citation	IEEE Transactions on Signal Processing, 2015, 63 (3), pp. 742 - 753	en_US
dc.identifier.issn	1053-587X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119076
dc.description.abstract	© 2015 IEEE. In Part I, a Matching Pursuit LASSO (MPL) algorithm has been presented for solving large-scale sparse recovery (SR) problems. In this paper, we present a subspace search to further improve the performance of MPL, and then continue to address another major challenge of SR-batch SR with many signals, a consideration which is absent from most of previous ℓ1-norm methods. A batch-mode MPL is developed to vastly speed up sparse recovery of many signals simultaneously. Comprehensive numerical experiments on compressive sensing and face recognition tasks demonstrate the superior performance of MPL and BMPL over other methods considered in this paper, in terms of sparse recovery ability and efficiency. In particular, BMPL is up to 400 times faster than existing ℓ1-norm methods considered to be state-of-the-art.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT130100746
dc.relation.ispartof	IEEE Transactions on Signal Processing	en_US
dc.relation.isbasedon	10.1109/TSP.2014.2385660	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Matching pursuit LASSO part II: Applications and sparse recovery over batch signals	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	63	en_US
utslib.for	090609 Signal Processing	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	63	en_US

Abstract:

© 2015 IEEE. In Part I, a Matching Pursuit LASSO (MPL) algorithm has been presented for solving large-scale sparse recovery (SR) problems. In this paper, we present a subspace search to further improve the performance of MPL, and then continue to address another major challenge of SR-batch SR with many signals, a consideration which is absent from most of previous ℓ1-norm methods. A batch-mode MPL is developed to vastly speed up sparse recovery of many signals simultaneously. Comprehensive numerical experiments on compressive sensing and face recognition tasks demonstrate the superior performance of MPL and BMPL over other methods considered in this paper, in terms of sparse recovery ability and efficiency. In particular, BMPL is up to 400 times faster than existing ℓ1-norm methods considered to be state-of-the-art.

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http://hdl.handle.net/10453/119076