publication . Preprint . Article . 2015

Dirichlet Process Parsimonious Mixtures for clustering

Faicel Chamroukhi;
Open Access English
  • Published: 14 Jan 2015
Abstract
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametric Bayesian prospective. We propose new Dirichlet Process Parsimonious mixtures (DPPM) which represent a Bayesian nonparametric formulation of these parsimonious Gaussian mixture models. The proposed DPPM models are Bayesian nonparametric parsimonious mixture models that allow to simultaneously infer the model parameters, the optimal number of mixt...
Subjects
arXiv: Statistics::ComputationStatistics::Methodology
free text keywords: Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
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