NONLINEAR COMPONENT ANALYSIS AS A KERNEL EIGENVALUE PROBLEM

A new method for performing a nonlinear form of principal component an alysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional f eature spaces, related to input space by some nonlinear map-for instan ce, the space of all possible five-pixel products in 16 x 16 images. W e give the derivation of the method and present experimental results o n polynomial feature extraction for pattern recognition.