We consider the following k-sparse recovery problem: design an m ? n matrix A, such that for any signal x, given Ax we can efficiently recover ^x satisfying
This paper addresses an innovative approach to informed enhancement of damaged sound. It uses sparse approximations with a learned dictionary of atoms modeling the main components...
Manuel Moussallam, Pierre Leveau, Si-Mohamed Aziz ...
Abstract—The Dantzig selector is a recently introduced technique for near-optimal estimatation of sparse signals from a limited set of measurements. This paper offers an interpre...
We study the problem of estimating the best k term Fourier representation for a given frequency-sparse signal (i.e., vector) A of length N k. More explicitly, we investigate how t...
Abstract. Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this...