25 search results - page 1 / 5 » Learning GP-BayesFilters via Gaussian process latent variabl... |
AROBOTS
2011
13 years 4 days ago
2011
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
DAGM
2010
Springer
13 years 6 months ago
2010
Springer
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...
MLMI
2007
Springer
13 years 11 months ago
2007
Springer
We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM) [1]...
ICTAI
2005
IEEE
13 years 10 months ago
2005
IEEE
In this paper, we propose a novel stochastic framework for unsupervised manifold learning. The latent variables are introduced, and the latent processes are assumed to characteriz...