A dynamic merge-or-split learning algorithm on gaussian mixture for automated model selection

Authors:
Jinwen Ma;Qicai He
Affiliations:
Department of Information Science, School of Mathematical, Sciences and LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical, Sciences and LMAM, Peking University, Beijing, China
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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Abstract

Gaussian mixture modelling is a powerful tool for data analysis. However, the selection of number of Gaussians in the mixture, i.e., the mixture model or scale selection, remains a difficult problem. In this paper, we propose a new kind of dynamic merge-or-split learning (DMOSL) algorithm on Gaussian mixture such that the number of Gaussians can be determined automatically with a dynamic merge-or-split operation among estimated Gaussians from the EM algorithm. It is demonstrated by the simulation experiments that the DMOSL algorithm can automatically determine the number of Gaussians in a sample data set, and also lead to a good estimation of the parameters in the original mixture. Moreover, the DMOSL algorithm is applied to the classification of Iris data.