Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/101064
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Type: Journal article
Title: Matching pursuit LASSO part II: applications and sparse recovery over batch signals
Author: Tan, M.
Tsang, I.W.
Wang, L.
Citation: IEEE Transactions on Signal Processing, 2015; 63(3):742-753
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 1053-587X
1941-0476
Statement of
Responsibility: 
Mingkui Tan, Ivor W. Tsang, and Li Wang
Abstract: 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.
Keywords: Batch mode LASSO; big dictionary; compressive sensing; face recognition; sparse recovery
Rights: © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030021099
DOI: 10.1109/TSP.2014.2385660
Grant ID: http://purl.org/au-research/grants/arc/FT130100746
http://purl.org/au-research/grants/arc/DE120101161
http://purl.org/au-research/grants/arc/DP140102270
Appears in Collections:Computer Science publications

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