k-MLE for mixtures of generalized Gaussians

We introduce an extension of the k-MLE algorithm, a fast algorithm for learning statistical mixture models relying on maximum likelihood estimators, which allows to build mixture of generalized Gaussian distributions without a fixed shape parameter. This allows us to model finely probability density...

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Bibliographische Detailangaben

Hauptverfasser:	Schwander, O., Schutz, A. J., Nielsen, F., Berthoumieu, Y.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Clustering algorithms Computational modeling Convergence Cost function Gaussian distribution Maximum likelihood estimation Shape
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