Matching pursuit LASSO part II: Applications and sparse recovery over batch signals

Publication Type:
Journal Article
IEEE Transactions on Signal Processing, 2015, 63 (3), pp. 742 - 753
Issue Date:
Filename Description Size
06998859.pdfPublished Version2.31 MB
Adobe PDF
Full metadata record
© 2015 IEEE. 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.
Please use this identifier to cite or link to this item: