Fast progressive training of mixture models for model selection

Finite mixture models (FMM) are flexible models with varying uses such as density estimation, clustering, classification, modeling heterogeneity, model averaging, and handling missing data. Expectation maximization (EM) algorithm can learn the maximum likelihood estimates for the model parameters. One of the prerequisites for using the EM algorithm is the a priori knowledge of the number of mixture components in the mixture model. However, the number of mixing components is often unknown. Therefore, determining the number of mixture components has been a central problem in mixture modelling. Thus, mixture modelling is often a two-stage process of determining the number of mixture components and then estimating the parameters of the mixture model. This paper proposes a fast training of a series of mixture models using progressive merging of mixture components to facilitate model selection algorithm to make appropriate choice of the model. The paper also proposes a data driven, fast approximation of the Kullback–Leibler (KL) divergence as a criterion to measure the similarity of the mixture components. We use the proposed methodology in mixture modelling of a synthetic dataset, a publicly available zoo dataset, and two chromosomal aberration datasets showing that model selection is efficient and effective.