Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Matching pursuit LASSO part II: applications and sparse recovery over batch signals|
|Citation:||IEEE Transactions on Signal Processing, 2015; 63(3):742-753|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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.|
|Appears in Collections:||Aurora harvest 3|
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.