The Two Stage Approach to the Compressed Sensing Problem
Abstract
This paper gives new results on the recovery of sparse signals using -norm minimization. We introduce a two-stage algorithm. The first step consists of the standard relaxation. The second step consists of optimizing the norm of a subvector whose components are indexed by the largest components in the first stage. If is set to , an intuitive choice motivated by the fact that is an empirical breakdown point for the plain exact recovery probability curve, Monte Carlo simulations show that the two-stage method outperforms the plain .
View on arXivComments on this paper