We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary. Connections with sparse signal recovery allows for the use of efficient reconstruction techniques such as Basis Pursuit. Of particular interest is the dictionary of time-frequency shift matrices and its role for channel estimation and identification in communications engineering. We present recovery results for Basis Pursuit with the time-frequency shift dictionary and various dictionaries of random matrices. I
Udgivelsesdato: JANWe consider the problem of recovering a structured sparse representation of a sig...
International audienceWe consider the problem of recovering a structured sparse representation of a ...
In a series of recent results, several authors have shown that both l¹-minimization (Basis Pursuit) ...
We describe a connection between the identification problem for matrices with sparse representations...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
In this paper, we survey algorithms for sparse recovery problems that are based on sparse random mat...
Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to sol...
We consider signals and operators in finite dimension which have sparse time-frequency representatio...
This dissertation focuses on sparse representation and dictionary learning, with three relative topi...
International audienceFinding a sparse approximation of a signal from an arbitrary dictionary is a v...
This article treats the problem of learning a dictionary providing sparse representations for a give...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
Abstract — This note reports a comparison result of twelve typical sparse signal recovery algorithms...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
Sparsity as a powerful instrument in signal processing is now commonplace. However, it is also well ...
Udgivelsesdato: JANWe consider the problem of recovering a structured sparse representation of a sig...
International audienceWe consider the problem of recovering a structured sparse representation of a ...
In a series of recent results, several authors have shown that both l¹-minimization (Basis Pursuit) ...
We describe a connection between the identification problem for matrices with sparse representations...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
In this paper, we survey algorithms for sparse recovery problems that are based on sparse random mat...
Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to sol...
We consider signals and operators in finite dimension which have sparse time-frequency representatio...
This dissertation focuses on sparse representation and dictionary learning, with three relative topi...
International audienceFinding a sparse approximation of a signal from an arbitrary dictionary is a v...
This article treats the problem of learning a dictionary providing sparse representations for a give...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
Abstract — This note reports a comparison result of twelve typical sparse signal recovery algorithms...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
Sparsity as a powerful instrument in signal processing is now commonplace. However, it is also well ...
Udgivelsesdato: JANWe consider the problem of recovering a structured sparse representation of a sig...
International audienceWe consider the problem of recovering a structured sparse representation of a ...
In a series of recent results, several authors have shown that both l¹-minimization (Basis Pursuit) ...