58 search results - page 9 / 12 » A Subspace Kernel for Nonlinear Feature Extraction |
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...
NIPS
2008
13 years 8 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...
CVPR
2012
IEEE
11 years 9 months ago
2012
IEEE
This paper presents a general multi-view feature extraction approach that we call Generalized Multiview Analysis or GMA. GMA has all the desirable properties required for cross-vi...
TCSV
2008
13 years 6 months ago
2008
Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonst...
SMC
2007
IEEE
14 years 29 days ago
2007
IEEE
—We carried out a series of experiments on text classification using multi-word features. An automated method was proposed to extract the multi-words from text data set and two d...