Likelihood Maximization and Moment Matching in Low <scp>SNR</scp> Gaussian Mixture Models

Penalized Likelihood Estimation in Gaussian Mixture Models

Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching

The Gaussian mixture model is a classic technique for clustering and data modeling that is used in numerous applications. With the rise of big data, there is a need for parameter estimation techniques that can handle streaming data and distribute the computation over several processors. While online variants of the Expectation Maximization (EM) algorithm exist, their data efficiency is reduced ...

Maximum likelihood estimation of Gaussian mixture models using stochastic search

Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the data. The conventional expectation–maximization (EM) algorithm for the maximum likelihood estimation of the parameters of GMMs is very sensitive to initialization and easily gets trapped in local maxima. Stochastic search algorithms have been popular alternati...

Mixture Models and Expectation-Maximization

This tutorial attempts to provide a gentle introduction to EM by way of simple examples involving maximum-likelihood estimation of mixture-model parameters. Readers familiar with ML paramter estimation and clustering may want to skip directly to Sections 5.2 and 5.3.

Design of Gaussian mixture models using matching pursuit

In this paper, a new design algorithm for estimating the parameters of Gaussian Mixture Models is presented. The method is based on the matching pursuit algorithm. Speaker Identification is considered as an application area. The estimated GMM performs as good as the EM algorithm based model. Computational complexity of the proposed method is much lower than the EM algorithm.