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The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning

Abstract : Incremental Expectation Maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each iteration. Nevertheless, these algorithms all assume that the conditional expectations of the sufficient statistics are explicit. In this paper, we propose a novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER), which builds on the Stochastic Path Integral Differential EstimatoR EM (SPIDER-EM) algorithm. The 3P-SPIDER algorithm addresses many intractabilities of the E-step of EM; it also deals with non-smooth regularization and convex constraint set. Numerical experiments show that 3P-SPIDER outperforms other incremental EM methods and discuss the role of some design parameters.
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Contributor : Gersende Fort Connect in order to contact the contributor
Submitted on : Monday, May 24, 2021 - 12:39:56 AM
Last modification on : Wednesday, June 1, 2022 - 4:28:39 AM


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  • HAL Id : hal-03183774, version 2


Gersende Fort, Eric Moulines. The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning. SSP 2021 - IEEE Statistical Signal Processing Workshop, Jul 2021, Rio de Janeiro, Brazil. ⟨hal-03183774v2⟩



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