The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters o...
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
This paper presents a novel motion segmentation algorithm on the basis of mixture of Dirichlet process (MDP) models, a kind of nonparametric Bayesian framework. In contrast to pre...
The latent class model or multivariate multinomial mixture is a powerful model for clustering discrete data. This model is expected to be useful to represent non-homogeneous popula...
Damien Tessier, Marc Schoenauer, Christophe Bierna...
In a previous work, the authors have introduced a Mixture of Laplacians model in order to cluster the observed data into the sound sources that exist in an underdetermined two-sens...