Matching Pursuit With Stochastic Selection

Abstract : In this paper, we propose a Stochastic Selection strategy that ac- celerates the atom selection step of Matching Pursuit. This strategy consists of randomly selecting a subset of atoms and a subset of rows in the full dictionary at each step of the Matching Pursuit to obtain a sub-optimal but fast atom selection. We study the performance of the proposed algorithm in terms of approximation accuracy (decrease of the residual norm), of exact-sparse recovery and of audio declipping of real data. Numerical experiments show the relevance of the ap- proach. The proposed Stochastic Selection strategy is presented with Matching Pursuit but applies to any pursuit algorithms provided that their selection step is based on the computation of correlations.
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Thomas Peel, Valentin Emiya, Liva Ralaivola, Sandrine Anthoine. Matching Pursuit With Stochastic Selection. European Signal Processing Conference EUSIPCO 2012, Aug 2012, Bucarest, Romania. pp.1-5. ⟨hal-00725075⟩

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