Estimating parameters of GMM based on split EM

The expectation maximization algorithm has been classically used to find the maximum likelihood estimates of parameters in mixture probabilistic models.Problems of the EM algorithm are that parameters initialization depends on some prior knowledge,and it is easy to converge to a local maximum in the iteration process.In this paper,a new method of estimating the parameter of GMM based on split EM is proposed,it starts from a single mixture component,sequentially split and estimates the parameter of the mixture components during expectation maximization steps.Extensive experiments show the advantages and efficiency of the proposed method.