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Incremental Expectation Maximization (EM) algorithms were introduced todesign EM for the large scale learning framework by avoiding the full data setto be processed at each iteration. Nevertheless, these algorithms all assumethat the conditional expectations of the sufficient statistics are explicit. Inthis paper, we propose a novel algorithm named Perturbed Prox-PreconditionedSPIDER (3P-SPIDER), which builds on the Stochastic Path Integral DifferentialEstimatoR EM (SPIDER-EM) algorithm. The 3P-SPIDER algorithm addresses manyintractabilities of the E-step of EM; it also deals with non-smoothregularization and convex constraint set. Numerical experiments show that3P-SPIDER outperforms other incremental EM methods and discuss the role of somedesign parameters.

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