Matrix analysis
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Probability (2nd ed.)
Deterministic annealing EM algorithm
Neural Networks
An experimental comparison of model-based clustering methods
Machine Learning
Computational Statistics & Data Analysis
Parabolic acceleration of the EM algorithm
Statistics and Computing
Using evolutionary algorithms for model-based clustering
Pattern Recognition Letters
Model-based clustering for multivariate functional data
Computational Statistics & Data Analysis
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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.