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DAGM
2010
Springer
14 years 11 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
AUTOMATICA
2010
167views more  AUTOMATICA 2010»
14 years 10 months ago
A new kernel-based approach for linear system identification
This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose s...
Gianluigi Pillonetto, Giuseppe De Nicolao
CVPR
2006
IEEE
15 years 11 months ago
Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking
An efficient Nonparametric Belief Propagation (NBP) algorithm is developed in this paper. While the recently proposed nonparametric belief propagation algorithm has wide applicati...
Tony X. Han, Huazhong Ning, Thomas S. Huang
ICASSP
2011
IEEE
14 years 1 months ago
Non-parametric bayesian measurement noise density estimation in non-linear filtering
In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Diri...
Emre Özkan, Saikat Saha, Fredrik Gustafsson, ...
ESSMAC
2003
Springer
15 years 3 months ago
Analysis of Some Methods for Reduced Rank Gaussian Process Regression
Abstract. While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational com...
Joaquin Quiñonero Candela, Carl Edward Rasm...