Neural Computation
July 1, 1998, Vol. 10, No. 5, Pages 1299-1319
(doi: 10.1162/089976698300017467)
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Article PDF (588.53 KB)
Abstract
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.