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Local orthogonal greedy pursuits for scalable sparse approximation of large signals with shift-invariant dictionaries

Abstract : We propose a way to increase the speed of greedy pursuit algorithms for scalable sparse signal approximation. It is designed for dictionaries with localized atoms, such as timefrequency dictionaries. When applied to OMP, our modification leads to an approximation as good as OMP while keeping the computation time close to MP. 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.
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https://hal.inria.fr/inria-00369531
Contributor : Ist Rennes <>
Submitted on : Friday, March 20, 2009 - 11:23:49 AM
Last modification on : Thursday, January 7, 2021 - 4:29:13 PM
Long-term archiving on: : Thursday, June 10, 2010 - 5:35:45 PM

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  • HAL Id : inria-00369531, version 1

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Boris Mailhé, Rémi Gribonval, Frédéric Bimbot, Pierre Vandergheynst. Local orthogonal greedy pursuits for scalable sparse approximation of large signals with shift-invariant dictionaries. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369531⟩

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