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Type: Journal article
Title: Matching pursuit LASSO part I: sparse recovery over big dictionary
Author: Tan, M.
Tsang, I.
Wang, L.
Citation: IEEE Transactions on Signal Processing, 2015; 63(3):727-741
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 1053-587X
Statement of
Mingkui Tan, Ivor W. Tsang, and Li Wang
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.
Keywords: convex programming; Sparse recovery; compressive sensing; LASSO; matching pursuit; big dictionary
Rights: © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030021098
DOI: 10.1109/TSP.2014.2385036
Grant ID:
Appears in Collections:Computer Science publications

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