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A New Nonconvex Sparse Recovery Method for Compressive Sensing

Authors :
Zhiyong Zhou
Jun Yu
Source :
Frontiers in Applied Mathematics and Statistics, Vol 5 (2019)
Publication Year :
2019
Publisher :
Frontiers Media S.A., 2019.

Abstract

As an extension of the widely used ℓr-minimization with 0 < r ≤ 1, a new non-convex weighted ℓr − ℓ1 minimization method is proposed for compressive sensing. The theoretical recovery results based on restricted isometry property and q-ratio constrained minimal singular values are established. An algorithm that integrates the iteratively reweighted least squares algorithm and the difference of convex functions algorithm is given to approximately solve this non-convex problem. Numerical experiments are presented to illustrate our results.

Details

Language :
English
ISSN :
22974687
Volume :
5
Database :
Directory of Open Access Journals
Journal :
Frontiers in Applied Mathematics and Statistics
Publication Type :
Academic Journal
Accession number :
edsdoj.4f834f2fb5e483e849f5d1accc1849d
Document Type :
article
Full Text :
https://doi.org/10.3389/fams.2019.00014