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An algorithm for estimating number of components of Gaussian mixture model based on penalized distance

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3 Author(s)
Daming Zhang ; Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, 230039, China ; Hui Guo ; Bin Luo

The expectation-maximization (EM) algorithm is a popular approach for parameter estimation of finite mixture model (FMM). A drawback of this approach is that the number of components of the finite mixture model is not known in advance, nevertheless, it is a key issue for EM algorithms. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Under the framework of Greedy EM, a fast and accurate algorithm for estimating the number of components of the Gaussian mixture model (GMM) is proposed. The performance of this algorithm is validated via simulative experiments of univariate and bivariate Gaussian mixture models.

Published in:

Neural Networks and Signal Processing, 2008 International Conference on

Date of Conference:

7-11 June 2008