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 s for a measurement ma...
special issue on Sparse Approximations in Signal and Image ProcessingInternational audienceGuest edi...
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of...
AbstractThis paper is an attempt to both expound and expand upon, from an approximation theorist’s p...
This book systematically presents recent fundamental results on greedy approximation with respect to...
Sparse representations of a function is a very powerful tool to analyze and approximate the function...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
This unique text/reference presents a comprehensive review of the state of the art in sparse represe...
We consider list versions of sparse approximation problems, where unlike the existing results in spa...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract—We show that girth can be used to certify that sparse compressed sensing matrices have good...
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we ...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
special issue on Sparse Approximations in Signal and Image ProcessingInternational audienceGuest edi...
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of...
AbstractThis paper is an attempt to both expound and expand upon, from an approximation theorist’s p...
This book systematically presents recent fundamental results on greedy approximation with respect to...
Sparse representations of a function is a very powerful tool to analyze and approximate the function...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
This unique text/reference presents a comprehensive review of the state of the art in sparse represe...
We consider list versions of sparse approximation problems, where unlike the existing results in spa...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract—We show that girth can be used to certify that sparse compressed sensing matrices have good...
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we ...
In this correspondence, we introduce a sparse approximation property of order for a measurement matr...
In this correspondence, we introduce a sparse approximation property of order s for a measurement ma...
special issue on Sparse Approximations in Signal and Image ProcessingInternational audienceGuest edi...
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of...