54 search results - page 1 / 11 » Dirichlet Process Gaussian Mixture Models: Choice of the Bas... |
JCST
2010
13 years 3 months ago
2010
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
ICIP
2007
IEEE
14 years 6 months ago
2007
IEEE
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
ICASSP
2009
IEEE
13 years 2 months ago
2009
IEEE
In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
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...
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...