Publications associées à Orthogonal basis

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Dictionary Identifiability from Few Training 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, or more precisely the problem of identifying a dictionary

dico

from a set of training samples

Y

knowing that $ ...

2008

Orthogonal basis

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Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

An updated version of wannier90: A tool for obtaining maximally-localised Wannier functions

Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods

Pseudopotential formalism for fractional Chern insulators

Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques

Spread Spectrum for Universal Compressive Sampling

Electromagnetic modes of a disordered photonic crystal

Multichannel Compressed Sensing via Source Separation for Hyperspectral Images

Distributed Compressed Sensing of Hyperspectral Images via Blind Source Separation

Dictionary Identifiability from Few Training Samples

Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

An updated version of wannier90: A tool for obtaining maximally-localised Wannier functions

Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods

Pseudopotential formalism for fractional Chern insulators

Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques

Spread Spectrum for Universal Compressive Sampling

Electromagnetic modes of a disordered photonic crystal

Distributed Compressed Sensing of Hyperspectral Images via Blind Source Separation

Multichannel Compressed Sensing via Source Separation for Hyperspectral Images

Dictionary Identifiability from Few Training Samples