基于惩罚距离的混合模型分量数自动估计算法

Automatic Estimation Algorithm of Component Number of Mixture Model Based on Penalized Distance

期望最大化(EM)算法是对有限混合模型进行参数估计的通用算法,然而标准EM算法中所需的混合模型分量数往往是未知的.文中研究了一种采用惩罚性最小匹配距离估计分量数的方法,并结合贪婪EM算法框架,提出了一种可以在进行参数估计的同时快速准确地自动估计高斯混合模型分量数的算法,最后通过一元和二元高斯混合模型的仿真实验验证了该算法的有效性.

The expectation-maximization （EM） algorithm is a popular approach to the parameter estimation of the finite mixture model （FMM）. A drawback of this approach is that the number of components of the FMM is not known in advance. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Then, under the framework of Greedy EM, an automatic algorithm with high speed and accuracy is proposed to esti- mate the component number of the Gaussian mixture model. The effectiveness of the proposed algorithm is finally verified by the simulations of univariate and bivariate Gaussian mixture models.