Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Entropy-Based Variational Scheme for Fast Bayes Learning of Gaussian Mixtures
verfasst von : Antonio Peñalver, Francisco Escolano, Boyan Bonev
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper, we propose a fast entropy-based variational scheme for learning Gaussian mixtures. The key element of the proposal is to exploit the incremental learning approach to perform model selection through efficient iteration over the Variational Bayes (VB) optimization step in a way that the number of splits is minimized. In order to minimize the number of splits we only select for spliting the worse kernel in terms of evaluating its entropy. Recent Gaussian mixture learning proposals suggest the use of that mechanism if a bypass entropy estimator is available. Here we will exploit the recently proposed Leonenko estimator. Our experimental results, both in 2D and in higher dimension show the effectiveness of the approach which reduces an order of magnitude the computational cost of the state-of-the-art incremental component learners.