A Theoretical and Experimental Comparison of the EM and SEM Algorithm
Machine Learning
2014-07-03 v2 Machine Learning
Abstract
In this paper we provide a new analysis of the SEM algorithm. Unlike previous work, we focus on the analysis of a single run of the algorithm. First, we discuss the algorithm for general mixture distributions. Second, we consider Gaussian mixture models and show that with high probability the update equations of the EM algorithm and its stochastic variant are almost the same, given that the input set is sufficiently large. Our experiments confirm that this still holds for a large number of successive update steps. In particular, for Gaussian mixture models, we show that the stochastic variant runs nearly twice as fast.
Cite
@article{arxiv.1310.5034,
title = {A Theoretical and Experimental Comparison of the EM and SEM Algorithm},
author = {Johannes Blömer and Kathrin Bujna and Daniel Kuntze},
journal= {arXiv preprint arXiv:1310.5034},
year = {2014}
}
Comments
This paper is a preprint of a paper submitted to and accepted for publication in ICPR 2014 and is subject to IEEE copyright