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» Hierarchical Gaussian process latent variable models
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ICA
2007
Springer
15 years 10 months ago
Conjugate Gamma Markov Random Fields for Modelling Nonstationary Sources
In modelling nonstationary sources, one possible strategy is to define a latent process of strictly positive variables to model variations in second order statistics of the underly...
Ali Taylan Cemgil, Onur Dikmen
NIPS
2007
15 years 8 months ago
Gaussian Process Models for Link Analysis and Transfer Learning
In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for dir...
Kai Yu, Wei Chu
CVPR
2005
IEEE
16 years 8 months ago
2D Statistical Models of Facial Expressions for Realistic 3D Avatar Animation
We address the issue of modelling facial expressions for realistic 3D avatar animation. We introduce a hierarchical decomposition of a human face into different components and mod...
Lukasz Zalewski, Shaogang Gong
ICDM
2007
IEEE
289views Data Mining» more  ICDM 2007»
16 years 24 days ago
Latent Dirichlet Conditional Naive-Bayes Models
In spite of the popularity of probabilistic mixture models for latent structure discovery from data, mixture models do not have a natural mechanism for handling sparsity, where ea...
Arindam Banerjee, Hanhuai Shan
ICASSP
2010
IEEE
15 years 6 months ago
Hierarchical Gaussian Mixture Model
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
Vincent Garcia, Frank Nielsen, Richard Nock