Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/101063
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dc.contributor.author | Tan, M. | - |
dc.contributor.author | Tsang, I. | - |
dc.contributor.author | Wang, L. | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Signal Processing, 2015; 63(3):727-741 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.issn | 1941-0476 | - |
dc.identifier.uri | http://hdl.handle.net/2440/101063 | - |
dc.description.abstract | Large-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. | - |
dc.description.statementofresponsibility | Mingkui Tan, Ivor W. Tsang, and Li Wang | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.rights | © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | - |
dc.source.uri | http://dx.doi.org/10.1109/tsp.2014.2385036 | - |
dc.subject | convex programming; Sparse recovery; compressive sensing; LASSO; matching pursuit; big dictionary | - |
dc.title | Matching pursuit LASSO part I: sparse recovery over big dictionary | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TSP.2014.2385036 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT130100746 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE120101161 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140102270 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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