Date(s) : 13/09/2012 iCal
14 h 00 min - 15 h 00 min
Matching pursuit with stochastic selection (by Thomas Peel, LIF). 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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