318 search results - page 28 / 64 » Learning subspace kernels for classification |
PAMI
2002
13 years 7 months ago
2002
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
CVPR
2010
IEEE
13 years 11 months ago
2010
IEEE
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
ISNN
2007
Springer
14 years 1 months ago
2007
Springer
Manifold learning algorithms have been proven to be capable of discovering some nonlinear structures. However, it is hard for them to extend to test set directly. In this paper, a ...
IJCV
2006
13 years 7 months ago
2006
Subspacefacerecognitionoftensuffersfromtwoproblems:(1)thetrainingsamplesetissmallcompared with the high dimensional feature vector; (2) the performance is sensitive to the subspace...
ICPR
2008
IEEE
14 years 2 months ago
2008
IEEE
Traditional face superresolution methods treat face images as 1D vectors and apply PCA on the set of these 1D vectors to learn the face subspace. Zhang et al [7] proposed Two-dire...