Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Matching pursuit LASSO part I: sparse recovery over big dictionary|
|Citation:||IEEE Transactions on Signal Processing, 2015; 63(3):727-741|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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.|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.