Non-convex compressed sensing using partial support information

TitleNon-convex compressed sensing using partial support information
Publication TypeSINBAD Presentation
AuthorsNavid Ghadermarzy
PublisherSINBAD
Year of Publication2012
Abstract

In this talk, we will address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when (possibly inaccurate) partial support information is available. First we will motivate the use of (weighted) $\ell_p$ minimization with $p<1$ and point out its advantages over weighted $\ell_1$ minimization when there is prior information on the support of the signal that is possibly partial and inaccurate. Then we will provide theoretical guarantees of sufficient recovery conditions for weighted $\ell_p$ minimization, which are better than those for (unweighted) $\ell_p$ minimization as well as those for weighted $\ell_1$. In the last part of the talk, we will illustrate our results with some numerical experiments stylized applications.

KeywordsPresentation, SINBAD, SINBADFALL2012, SLIM
URLhttps://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2012/Fall/ghadermarzy2012SINBADncc/ghadermarzy2012SINBADncc_pres.pdf
Citation Keyghadermarzy2012SINBADncc