Initializing EM using the properties of its trajectories in Gaussian mixtures

Abstract

A strategy is proposed to initialize the EM algorithm in the multivariate Gaussian mixture context. It consists in randomly drawing, with a low computational cost in many situations, initial mixture parameters in an appropriate space including all possible EM trajectories. This space is simply defined by two relations between the two first empirical moments and the mixture parameters satisfied by any EM iteration. An experimental study on simulated and real data sets clearly shows that this strategy outperforms classical methods, since it has the nice property to widely explore local maxima of the likelihood function.