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» Hierarchical Gaussian process latent variable models
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EACL
2006
ACL Anthology
15 years 5 months ago
Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis
Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, th...
Ayman Farahat, Francine Chen
ICIP
2007
IEEE
16 years 5 months ago
3D Human Motion Tracking using Manifold Learning
This paper introduces a framework to track 3D human movement using Gaussian process dynamic model (GPDM) and particle filter. The framework combines the particle filter and discri...
Feng Guo, Gang Qian
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
15 years 2 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
GEOINFO
2007
15 years 5 months ago
Model Selection for a Class of Spatio-temporal Models for Areal Data
Abstract. We present a method to perform model selection based on predictive density in a class of spatio-temporal dynamic generalized linear models for areal data. These models as...
Juan C. Vivar, Marco A. R. Ferreira
CVPR
2009
IEEE
1132views Computer Vision» more  CVPR 2009»
16 years 11 months ago
Observable Subspaces for 3D Human Motion Recovery
The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. T...
Andrea Fossati (EPFL), Mathieu Salzmann (Universit...