921 search results - page 2 / 185 » Kernel Principal Component Analysis |
NIPS
2008
13 years 9 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...
ICIP
2005
IEEE
14 years 1 months ago
2005
IEEE
This paper presents a view independent face recognition method based on kernel principal component analysis (KPCA) of local parts. View changes induce large variation in feature s...
ECCV
2004
Springer
14 years 9 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 ...
PR
2006
2006
Correspondence matching using kernel principal components analysis and label consistency constraints
13 years 7 months ago
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how ker...
ICANN
1997
Springer
13 years 11 months ago
1997
Springer
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...