A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map-for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
2) Kernel principal component analysis (KPCA) REF is an extension of principal component analysis (PCA) using kernel methods.
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Neural Computation
Neural Computation
Computer Science Mathematics