Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03183774
Contributor : Gersende Fort <>
Submitted on : Sunday, March 28, 2021 - 10:25:50 PM
Last modification on : Tuesday, May 11, 2021 - 12:00:50 PM

Files

FM_HAL.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03183774, version 1

Citation

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

Share

Metrics

Record views

25

Files downloads

25