Model Selection for Gaussian Mixture Models
Methodology
2013-01-17 v1 Statistics Theory
Machine Learning
Statistics Theory
Abstract
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified EM algorithm is developed to simultaneously select the number of components and to estimate the mixing weights, i.e. the mixing probabilities, and unknown parameters of Gaussian distributions. Simulations and a real data analysis are presented to illustrate the performance of the proposed method.
Keywords
Cite
@article{arxiv.1301.3558,
title = {Model Selection for Gaussian Mixture Models},
author = {Tao Huang and Heng Peng and Kun Zhang},
journal= {arXiv preprint arXiv:1301.3558},
year = {2013}
}