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.
Scholkopf et al REF conducted a nonlinear form of principal component analysis (PCA) in high-dimensional feature spaces.
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Neural Computation
Neural Computation
Computer Science Mathematics