52 search results - page 5 / 11 » Nonlinear kernel density principal component analysis with a... |
KDD
2003
ACM
14 years 7 months ago
2003
ACM
To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected ...
ICML
2007
IEEE
14 years 7 months ago
2007
IEEE
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
JMLR
2006
13 years 6 months ago
2006
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
PAMI
2012
11 years 9 months ago
2012
— Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Lap...
JCP
2008
13 years 6 months ago
2008
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...