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

arXiv: https://arxiv.org/abs/2105.11732 · HTML: https://mlsanity.com/paper/2105.11732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 4 of 4)

- [A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization](https://mlsanity.com/paper/1802.04477/) (2018)
- [On the Global Convergence of (Fast) Incremental Expectation Maximization Methods](https://mlsanity.com/paper/1910.12521/) (2019)
- [Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization](https://mlsanity.com/paper/2011.12392/) (2020)
- [Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence](https://mlsanity.com/paper/2012.14670/) (2020)

## Built on by (most cited first, 1 of 1)

- [Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning](https://mlsanity.com/paper/2302.11147/) (2023)

## Abstract (arXiv)

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.