A low complexity Orthogonal Matching Pursuit for sparse signal approximation with shift-invariant dictionariesReportar como inadecuado




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1 METISS - Speech and sound data modeling and processing IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique 2 LTS2 - EPFL

Abstract : We propose a variant of Orthogonal Matching Pursuit OMP, called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to Matching Pursuit. Numerical experiments with a large audio signal show that, compared to OMP and Gradient Pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.

Keywords : sparse approximation greedy algorithms shift-invariance orthogonal matching pursuit





Autor: Boris Mailhé - Rémi Gribonval - Pierre Vandergheynst - Frédéric Bimbot -

Fuente: https://hal.archives-ouvertes.fr/



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