A stochastic 3MG algorithm with application to 2d filter identification

Abstract : Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often un-known a priori or their direct computation is too intensive, and they have to be estimated online from the related signals. In the context of batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g. a sparsity promoting function), Majorize-Minimize (MM) subspace methods have recently attracted much interest since they are fast, highly flexible and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the cost function is replaced by a sequence of stochastic approximations of it. Simulation results illustrate the good practical performance of the proposed MM Memory Gradient (3MG) algorithm when applied to 2D filter identification.
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Communication dans un congrès
22th European Signal Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. 2014
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Emilie Chouzenoux, Jean-Christophe Pesquet, Anisia Florescu. A stochastic 3MG algorithm with application to 2d filter identification. 22th European Signal Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. 2014. 〈hal-01077261〉

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