Challenging Restricted Isometry Constants with Greedy Pursuit
Language
en
Communication dans un congrès
This item was published in
Proc. of IEEE Information Theory Workshop 2009, Proc. of IEEE Information Theory Workshop 2009, ITW'09, 2009-10-11, Taormina. 2009-10-11p. 475-479
IEEE
English Abstract
This paper proposes greedy numerical schemes to compute lower bounds of the restricted isometry constants that are central in compressed sensing theory. Matrices with small restricted isometry constants enable stable ...Read more >
This paper proposes greedy numerical schemes to compute lower bounds of the restricted isometry constants that are central in compressed sensing theory. Matrices with small restricted isometry constants enable stable recovery from a small set of random linear measurements. We challenge this compressed sampling recovery using greedy pursuit algorithms that detect ill-conditionned sub-matrices. It turns out that these sub-matrices have large isometry constants and hinder the performance of compressed sensing recovery.Read less <
English Keywords
Compressed sensing
compressive sampling
random matrices
restricted isometry constants
sparsity
Origin
Hal imported