Improved initialization of the EM algorithm for mixture model parameter estimation
Panić, Branislav (Author), Klemenc, Jernej (Author), Nagode, Marko (Author)

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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.

Keywords:mixture model, parameter estimation, EM algorithm, REBMIX algorithm, density estimation, clustering, image segmentation
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:Str. 1-29
Numbering:Vol. 8, iss. 3
ISSN on article:2227-7390
DOI:10.3390/math8030373 This link opens in a new window
COBISS.SI-ID:17112347 This link opens in a new window
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Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0182
Name:Razvojna vrednotenja

Secondary language

Keywords:mešani model, ocena parametrov, EM algoritem, REBMIX algoritem, ocena gostote, porazdelitev verjetnosti, grozdenje, segmentacija slik

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