Publications associées à Algorithms and methods for sparse approximation in structured dictionaries

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Basis Identification from Random Sparse Samples

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

minimisation. The problem is to identify a dictionary

\dico

from a set of training samples

\Y

knowing that

\Y = \dico \X

...

2009

Algorithms and methods for sparse approximation in structured dictionaries

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Compressive sensing recovery of spike trains using a structured sparsity model

Geometric Video Approximation Using Weighted Matching Pursuit

Compressed sensing for radio interferometry: spread spectrum imaging techniques

Recovery of clustered sparse signals from compressive measurements

Basis Identification from Random Sparse Samples

Semantic coding by supervised dimensionality reduction

Dictionary preconditioning for greedy algorithms

Compressed sensing and redundant dictionaries

On finding approximate nearest neighbours in a set of compressible signals

Recovery of clustered sparse signals from compressive measurements

Semantic coding by supervised dimensionality reduction

Compressed sensing and redundant dictionaries

Geometric Video Approximation Using Weighted Matching Pursuit

Compressed sensing for radio interferometry: spread spectrum imaging techniques

Basis Identification from Random Sparse Samples

On finding approximate nearest neighbours in a set of compressible signals

Spread spectrum for imaging techniques in radio interferometry and strings detection

Compressive sensing recovery of spike trains using a structured sparsity model

Dictionary preconditioning for greedy algorithms