14 search results - page 2 / 3 » Regularized Locality Preserving Learning of Pre-Image Proble... |
JMLR
2010
12 years 11 months ago
2010
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
ICCV
2007
IEEE
14 years 6 months ago
2007
IEEE
Subspace learning based face recognition methods have attracted considerable interests in recent years, including Principal Component Analysis (PCA), Linear Discriminant Analysis ...
ICPR
2006
IEEE
14 years 5 months ago
2006
IEEE
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
ICCV
2005
IEEE
14 years 6 months ago
2005
IEEE
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
ICDM
2003
IEEE
13 years 10 months ago
2003
IEEE
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...