138 search results - page 1 / 28 » Latent Process Model for Manifold Learning |
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 ...
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...
CVPR
2011
IEEE
12 years 8 months ago
2011
IEEE
We propose a novel nonlinear, probabilistic and variational method for adding shape information to level setbased segmentation and tracking. Unlike previous work, we represent sha...
ICIP
2007
IEEE
14 years 6 months ago
2007
IEEE
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...
ICML
2007
IEEE
14 years 5 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...