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...
ICA
2004
Springer
2004
Springer
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
13 years 10 months ago
The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Becau...
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...