Matching pursuit LASSO part I: Sparse recovery over big dictionary

Tan, M; Tsang, IW; Wang, L

Matching pursuit LASSO part I: Sparse recovery over big dictionary

Tan, M Tsang, IW

Wang, L

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Signal Processing, 2015, 63 (3), pp. 727 - 741
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. 727 - 741	en_US
dc.identifier.issn	1053-587X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119075
dc.description.abstract	© 2015 IEEE. Large-scale sparse recovery (SR) by solving ℓ1-norm relaxations over Big Dictionary is a very challenging task. Plenty of greedy methods have therefore been proposed to address big SR problems, but most of them require restricted conditions for the convergence. Moreover, it is non-trivial for them to incorporate the ℓ1-norm regularization that is required for robust signal recovery. We address these issues in this paper by proposing a Matching Pursuit LASSO (MPL) algorithm, based on a novel quadratically constrained linear program (QCLP) formulation, which has several advantages over existing methods. Firstly, it is guaranteed to converge to a global solution. Secondly, it greatly reduces the computation cost of the ℓ1-norm methods over Big Dictionaries. Lastly, the exact sparse recovery condition of MPL is also investigated.	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.2014.2385036	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Matching pursuit LASSO part I: Sparse recovery over big dictionary	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	63	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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. Large-scale sparse recovery (SR) by solving ℓ1-norm relaxations over Big Dictionary is a very challenging task. Plenty of greedy methods have therefore been proposed to address big SR problems, but most of them require restricted conditions for the convergence. Moreover, it is non-trivial for them to incorporate the ℓ1-norm regularization that is required for robust signal recovery. We address these issues in this paper by proposing a Matching Pursuit LASSO (MPL) algorithm, based on a novel quadratically constrained linear program (QCLP) formulation, which has several advantages over existing methods. Firstly, it is guaranteed to converge to a global solution. Secondly, it greatly reduces the computation cost of the ℓ1-norm methods over Big Dictionaries. Lastly, the exact sparse recovery condition of MPL is also investigated.

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http://hdl.handle.net/10453/119075