A Hybrid Particle Swarm Optimization Approach to Bernoulli Mixture Models

Abstract

The use of mixture models in statistical analysis is increasing for dataset with heterogeneity and/or redundancy in the data. They are likelihood based models, and maximum likelihood estimates of parameters are attained by the use of the expectation maximization (EM) algorithm. Multi-modality of the likelihood surface means that the EM algorithm is highly dependent on starting points and poorly chosen initial points for the optimization may lead to only a local maximum, not a global maximum. The aim of this paper is to introduce a hybrid method of Particle Swarm Optimization (PSO) as a global optimization approach and the EM algorithm as a local search to overcome this problem and then it will be compared with different methods of choosing starting points in the EM algorithm.