BEHAVIOR OF GREEDY SPARSE REPRESENTATION ALGORITHMS ON NESTED SUPPORTS

Abstract : In this work, we study the links between the recovery proper- ties of sparse signals for Orthogonal Matching Pursuit (OMP) and the whole General MP class over nested supports. We show that the optimality of those algorithms is not locally nested: there is a dictionary and supports I and J with J included in I such that OMP will recover all signals of sup- port I, but not all signals of support J. We also show that the optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s′-sparse signals with s′ smaller than s. We also provide a tighter version of Donoho and Elad's spark theorem, which allows us to com- plete Tropp's proof that sparse representation algorithms can only be optimal for all s-sparse signals if s is strictly lower than half the spark of the dictionary.
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Communication dans un congrès
ICASSP - 38th International Conference on Acoustics, Speech and Signal Processing - 2013, May 2013, Vancouver, Canada. IEEE, 2013
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Contributeur : Boris Mailhé <>
Soumis le : vendredi 21 juin 2013 - 18:47:33
Dernière modification le : mardi 24 avril 2018 - 16:16:02
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Boris Mailhé, Bob Sturm, Mark Plumbley. BEHAVIOR OF GREEDY SPARSE REPRESENTATION ALGORITHMS ON NESTED SUPPORTS. ICASSP - 38th International Conference on Acoustics, Speech and Signal Processing - 2013, May 2013, Vancouver, Canada. IEEE, 2013. 〈hal-00837002〉

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