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Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm
Authors:Hai xian Wang  Bin Luo  Quan bing Zhang  Sui Wei
Affiliation:

Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Huangshan Road, Hefei 230039, People’s Republic of China

Abstract:The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.
Keywords:Finite mixture model  EM algorithm  Number of components  Model selection  SSMEM algorithm
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