A Dynamic Merge-or-Split Learning Algorithm on Gaussian Mixture for Automated Model Selection

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.