AbstractThis paper is an attempt to both expound and expand upon, from an approximation theorist’s point of view, some of the theoretical results that have been obtained in the sparse representation (compressed sensing) literature. In particular, we consider in detail ℓ1m-approximation, which is fundamental in the theory of sparse representations, and the connection between the theory of sparse representations and certain n-width concepts. We try to illustrate how the theory of sparse representation leads to new and interesting problems in approximation theory, while the results and techniques of approximation theory can further add to the theory of sparse representations
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
Relation between a family of generalized Support Vector Machine (SVM) problems and the novel ϵ-spars...
We address the problem of finding sparse wavelet representations of high-dimensional vectors. We pr...
AbstractThis paper is an attempt to both expound and expand upon, from an approximation theorist’s p...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
We show that girth can be used to certify that sparse compressed sensing matrices have good sparse a...
In sparse approximation problems, the goal is to find an approximate representation of a target sig...
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
International audienceSparse approximation addresses the problem of approximately fitting a linear m...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
International audienceWe extend recent results regarding the restricted isometry constants (RIC) and...
This book systematically presents recent fundamental results on greedy approximation with respect to...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
Relation between a family of generalized Support Vector Machine (SVM) problems and the novel ϵ-spars...
We address the problem of finding sparse wavelet representations of high-dimensional vectors. We pr...
AbstractThis paper is an attempt to both expound and expand upon, from an approximation theorist’s p...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
We show that girth can be used to certify that sparse compressed sensing matrices have good sparse a...
In sparse approximation problems, the goal is to find an approximate representation of a target sig...
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
International audienceSparse approximation addresses the problem of approximately fitting a linear m...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
International audienceWe extend recent results regarding the restricted isometry constants (RIC) and...
This book systematically presents recent fundamental results on greedy approximation with respect to...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
Relation between a family of generalized Support Vector Machine (SVM) problems and the novel ϵ-spars...
We address the problem of finding sparse wavelet representations of high-dimensional vectors. We pr...