Description: A new method for performing a nonlinear form of Principal
Component Analysis proposed. By the use of integral operator kernel
functions, one can eciently compute principal components in high{
dimensional feature spaces, related to input space by some nonlinear
map for instance the space of all possible d{pixel products in images.
We give the derivation of the method and present experimental results
on polynomial feature extraction for pattern recognition
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File list (Check if you may need any files):
KPCA\kernelPCA_scholkopf.pdf
....\kernelpca_tutorial.m
....\kpca-2.m
....\kpca.m
....\license.txt
....\scholkopf_kernel.pdf
KPCA
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