39 search results - page 1 / 8 » Evaluating probabilities under high-dimensional latent varia... |
NIPS
2003
14 years 8 days ago
2003
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...
NIPS
2008
14 years 10 days ago
2008
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is bas...
DAGM
2010
Springer
14 years 15 hour 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...
KDD
1998
ACM
14 years 3 months ago
1998
ACM
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
ICIP
2007
IEEE
15 years 18 days ago
2007
IEEE
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temp...