Challenging Restricted Isometry Constants with Greedy Pursuit
Langue
en
Communication dans un congrès
Ce document a été publié dans
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
Résumé en anglais
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 ...Lire la suite >
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.< Réduire
Mots clés en anglais
Compressed sensing
compressive sampling
random matrices
restricted isometry constants
sparsity
Origine
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