Optimization of Mixture Models: Comparison of Different Strategies
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Mixture model parameters are usually computed with maximum likelihood using an Expectation-Maximization (EM) algorithm. However, it is wellknown that this method can sometimes converge toward a critical point of the solution space which is not the global maximum. To minimize this problem, different strategies using different combinations of algorithms can be used. In this paper, we compare by the mean of numerical simulations strategies using EM, Classification EM, Stochastic EM, and Genetic algorithms for the optimization of mixture models. Our results indicate that two-stage procedures combining both an exploration phase and an optimization phase provide the best results, especially when these methods axe applied on several sets of initial conditions rather than on one single starting point.
KeywordsMixture models Mixture Transition Distribution model (MTD) Expectation-Maximization (EM) algorithm Classification EM (CEM) Stochastic EM (SEM) Genetic Algorithm (GA)
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