148 search results - page 24 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
86
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CVPR
2006
IEEE
15 years 11 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
15 years 11 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
15 years 11 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
14 years 11 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...
105
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ICCV
2007
IEEE
15 years 11 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...