### Estimating parameters of GMM based on split EM

ZHONG Jinqin1，3, GU Lichuan2, TAN Jieqing3, LI Yingying3

1. 1.Department of Electronic and Information, Anhui University, Hefei 230031, China
2.School of Information and Computer, Anhui Agriculture University, Hefei 230036, China
3.School of Computer and Information, Hefei University of Technology, Hefei 230009, China
• Online:2012-12-01 Published:2012-11-30

### 基于分裂EM算法的GMM参数估计

1. 1.安徽大学 电子与信息系，合肥 230031
2.安徽农业大学 计算机信息学院，合肥 230036
3.合肥工业大学 计算机与信息学院，合肥 230009

Abstract: 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.