148 search results - page 24 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
Dynamic appearance is one of the most important cues for tracking and identifying moving people. However, direct modeling spatio-temporal variations of such appearance is often a ...
CVPR
2005
IEEE
14 years 9 months ago
2005
IEEE
Images and other high-dimensional data can frequently be characterized by a low dimensional manifold (e.g. one that corresponds to the degrees of freedom of the camera). Recently,...
CVPR
2007
IEEE
14 years 9 months ago
2007
IEEE
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
ESANN
2007
13 years 9 months ago
2007
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
ICCV
2007
IEEE
14 years 9 months ago
2007
IEEE
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...