Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
Computational Statistics & Data Analysis
Inference for multivariate normal mixtures
Journal of Multivariate Analysis
A computational strategy for doubly smoothed MLE exemplified in the normal mixture model
Computational Statistics & Data Analysis
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EM algorithms for multivariate normal mixture decomposition have been recently proposed in order to maximize the likelihood function in a constrained parameter space having no singularities and a reduced number of spurious local maxima. However, such approaches require some a priori information about the eigenvalues of the covariance matrices. The behavior of the EM algorithm near a degenerated solution is investigated. The obtained theoretical results would suggest a new kind of constraint based on the dissimilarity between two consecutive updates of the eigenvalues of each covariance matrix. The performances of such a ''dynamic'' constraint are evaluated on the grounds of some numerical experiments.