219 search results - page 5 / 44 » Learning the Dimensionality of Hidden Variables |
ICML
2007
IEEE
14 years 11 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 ...
SIBGRAPI
2005
IEEE
14 years 4 months ago
2005
IEEE
This article presents a new method for discovering hidden patterns in high-dimensional dataset resulting from image registration. It is based on true factor analysis, a statistica...
UAI
2003
14 years 6 days ago
2003
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...
ICML
2007
IEEE
14 years 11 months ago
2007
IEEE
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
NIPS
1996
14 years 5 days ago
1996
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...