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