81 search results - page 1 / 17 » Multi-Class Linear Feature Extraction by Nonlinear PCA |
ICPR
2000
IEEE
13 years 9 months ago
2000
IEEE
ICPR
2008
IEEE
13 years 11 months ago
2008
IEEE
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computati...
TSMC
2008
13 years 3 months ago
2008
Abstract-- Using the kernel trick idea and the kernels as features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be e...
NIPS
2008
13 years 6 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...
ICASSP
2008
IEEE
13 years 11 months ago
2008
IEEE
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality...