Unsupervised Learning Approaches for the Finite Mixture Models: EM versus MCMC

Abstract

The key problem in unsupervised learning of finite mixture models is to estimate the parameters of the models. The expectation-maximization (EM) and Markov Chain Monte Carlo (MCMC) are usually used. In this paper, we review these two algorithms and give the complete algorithm processes. We also comment their advantage and disadvantage.