# Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling

arXiv: https://arxiv.org/abs/1907.09164 · HTML: https://mlsanity.com/paper/1907.09164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Fitting large mixture models using stochastic component selection](https://mlsanity.com/paper/2110.04776/) (2021)
- [A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models](https://mlsanity.com/paper/2203.10186/) (2022)
- [Towards frugal unsupervised detection of subtle abnormalities in medical imaging](https://mlsanity.com/paper/2309.02458/) (2023)
- [Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts](https://mlsanity.com/paper/2601.19811/) (2026)
- [Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models](https://mlsanity.com/paper/2602.07997/) (2026)

## Abstract (arXiv)

To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation-Maximization algorithm for general latent variable models is proposed. For exponential models the algorithm is shown to be convergent under classicalconditions as the number of iterations increases. Numerical experiments illustrate the performance of the mini-batch algorithm in various models.In particular, we highlight that mini-batch sampling results in an important speed-up of the convergence of the sequence of estimators generated by the algorithm. Moreover, insights on the effect of the mini-batch size on the limit distribution are presented. Finally, we illustrate how to use mini-batch sampling in practice to improve results when a constraint on the computing time is given.