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ICASSP
2009
IEEE
15 years 10 months ago
Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features
In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partl...
Friedrich Faubel, John W. McDonough, Dietrich Klak...
CSDA
2010
208views more  CSDA 2010»
15 years 3 months ago
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
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...
Raffaele Argiento, Alessandra Guglielmi, Antonio P...
ICPR
2006
IEEE
16 years 4 months ago
Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model
In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial waveletbased denoising techniques, based on Bayesian leastsquares optimization...
Paul Scheunders, Steve De Backer
JMLR
2000
134views more  JMLR 2000»
15 years 3 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
Marina Meila, Michael I. Jordan
JMLR
2010
218views more  JMLR 2010»
14 years 10 months ago
Simple Exponential Family PCA
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
Jun Li, Dacheng Tao