ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
KDD
2010
ACM
13 years 2 months ago
2010
ACM
One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we ...
CSDA
2010
13 years 4 months ago
2010
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses t...
ICPR
2010
IEEE
13 years 8 months ago
2010
IEEE
—In Dirichlet process (DP) mixture models, the number of components is implicitly determined by the sampling parameters of Dirichlet process. However, this kind of models usually...
KDD
2008
ACM
14 years 4 months ago
2008
ACM
Recent work in deduplication has shown that collective deduplication of different attribute types can improve performance. But although these techniques cluster the attributes col...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We introduce a new inference algorithm for Dirichlet process mixture models. While Gibbs sampling and variational methods focus on local moves, the new algorithm makes more global...
ECCV
2006
Springer
14 years 6 months ago
2006
Springer
A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assi...