Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics

Jernej Klemenc¹  Marko Nagode¹  Branislav Panić¹ 

[1] Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva ulica 6, 1000 Ljubljana, Slovenia;

关键词: mixture model;    parameter estimation;    em algorithm;    rebmix algorithm;    density estimation;    clustering;    image segmentation;   

DOI  :  10.3390/math8030373

来源: DOAJ

【 摘 要 】

A commonly used tool for estimating the parameters of a mixture model is the Expectation−Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density−estimation datasets and image−segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

【 授权许可】

Unknown