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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

Acquisition comprimée

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A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

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On unifying sparsity and geometry for image-based 3D scene representation

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Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

Conditions for recovery of sparse signals correlated by local transforms

Distributed Compressed Sensing for Sensor Networks Using Thresholding

Sparsity & [and] dictionaries - algorithms & [and] design

Time Resolved Fluorescence Diffuse Optical Tomography Using Multi-Resolution Exponential B-Splines

Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

Conditions for recovery of sparse signals correlated by local transforms

Linear Compressive Networks

A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

On unifying sparsity and geometry for image-based 3D scene representation

Basis Identification from Random Sparse Samples

Integrated Modeling And Reconstruction With Sparsity Constraints For fDOT