Beyond $\ell_1$ norm minimization for sparse signal recovery

TitleBeyond $\ell_1$ norm minimization for sparse signal recovery
Publication TypeConference
Year of Publication2012
AuthorsHassan Mansour
Conference Name2012 IEEE Statistical Signal Processing Workshop (SSP) (SSP'12)
Month03
OrganizationIEEE
Conference LocationAnn Arbor, Michigan, USA
Keywordscompressed sensing, iterative algorithms, partial support recovery, Sparse recovery, weighted $\ell_1$ minimization
Abstract

Sparse signal recovery has been dominated by the basis pursuit denoise (BPDN) problem formulation for over a decade. In this paper, we propose an algorithm that outperforms BPDN in finding sparse solutions to underdetermined linear systems of equations at no additional computational cost. Our algorithm, called WSPGL1, is a modification of the spectral projected gradient for $\ell_1$ minimization (SPGL1) algorithm in which the sequence of LASSO subproblems are replaced by a sequence of weighted LASSO subproblems with constant weights applied to a support estimate. The support estimate is derived from the data and is updated at every iteration. The algorithm also modifies the Pareto curve at every iteration to reflect the new weighted $\ell_1$ minimization problem that is being solved. We demonstrate through extensive simulations that the sparse recovery performance of our algorithm is superior to that of $\ell_1$ minimization and approaches the recovery performance of iterative re-weighted $\ell_1$ (IRWL1) minimization of Candès, Wakin, and Boyd. Moreover, our algorithm has the computational cost of a single BPDN problem.

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SSP/2012/mansour2012SSPwspgl1/mansour2012SSPwspgl1.pdf
Presentation

https://slim.gatech.edu/Publications/Public/Conferences/SSP/2012/mansour...

Citation Keymansour2012SSPwspgl1