文件名称:KPCA
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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-A new method for performing a nonlinear form of Principal
Component Analysis is 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
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-A new method for performing a nonlinear form of Principal
Component Analysis is 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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下载文件列表
KPCA\kernelPCA_scholkopf.pdf
....\kernelpca_tutorial.m
....\kpca-2.m
....\kpca.m
....\license.txt
....\scholkopf_kernel.pdf
KPCA