Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416958 | Computational Statistics & Data Analysis | 2011 | 11 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Salvatore Ingrassia, Roberto Rocci,