Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/101063
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dc.contributor.authorTan, M.en
dc.contributor.authorTsang, I.en
dc.contributor.authorWang, L.en
dc.date.issued2015en
dc.identifier.citationIEEE Transactions on Signal Processing, 2015; 63(3):727-741en
dc.identifier.issn1053-587Xen
dc.identifier.issn1941-0476en
dc.identifier.urihttp://hdl.handle.net/2440/101063-
dc.description.abstractLarge-scale sparse recovery (SR) by solving -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 -norm regularization that is required for robust signal recovery. We address these issues in this paper by proposing aMatching 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 -norm methods over Big Dictionaries. Lastly, the exact sparse recovery condition of MPL is also investigated.en
dc.description.statementofresponsibilityMingkui Tan, Ivor W. Tsang, and Li Wangen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.rights© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.subjectconvex programming; Sparse recovery; compressive sensing; LASSO; matching pursuit; big dictionaryen
dc.titleMatching pursuit LASSO part I: sparse recovery over big dictionaryen
dc.typeJournal articleen
dc.identifier.rmid0030021098en
dc.identifier.doi10.1109/TSP.2014.2385036en
dc.relation.granthttp://purl.org/au-research/grants/arc/FT130100746en
dc.relation.granthttp://purl.org/au-research/grants/arc/DE120101161en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102270en
dc.identifier.pubid170388-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS11en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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