208 search results - page 1 / 42 » Robust Kernel Principal Component Analysis |
NIPS
2008
13 years 10 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
ECCV
2004
Springer
14 years 11 months ago
2004
Springer
Abstract. We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the ...
WACV
2008
IEEE
14 years 3 months ago
2008
IEEE
In recent years, many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological in...
ICDM
2006
IEEE
14 years 3 months ago
2006
IEEE
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
CSDA
2010
13 years 9 months ago
2010
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...