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To improve the robustness of order selection and parameter learning for Gaussian mixture model (GMM), this paper proposes a competitive stop expectation-maximization (EM) algorithm, which is based on two stop conditions. The first condition is a Lilliefors test based multivariate (MV) normality criterion, which is used to determine whether to split a component into two different components. The EM algorithm stops splitting when all components have MV normality. The minimum description length (MDL) criterion is used in the second condition, which competes with the first condition to prevent the EM algorithm from over-splitting. Simulation experiments verify the effectiveness of the proposed algorithm.

Gaussian mixture modelling by exploiting competitive stop EM algorithm