A greedy merge learning algorithm for Gaussian mixture model

Authors:
Yan Li;Lei Li
Affiliations:
Department of Probability and Statistics, School of Mathematical Sciences and Computing Technology, Central South University, Changsha, China;Department of Information Science School of Mathematical Sciences and LAMA, Peking University, Beijing, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

Gaussian mixture model (GMM) has been widely used in fields of image processing and investment data mining. However, in many practical applications, the number of the components is not known. This paper proposes a kind of greedy merge EM (GMEM) learning algorithm such that the number of Gaussians can be determined automatically with the minimum message length (MML) criterion. Moreover, the greedy merge learning algorithm is successfully applied to unsupervised data analysis. It is demonstrated well by the experiments that the proposed greedy merge EM (GMEM) learning algorithm can make both parameter learning and decide the number of the Gaussian mixture.