Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1996)

by Bernhard Scholkopf. Alexander Smola. Klaus Robert Muller.

We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we 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 5{pixel products in 16 16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach and present rst experimental results on nonlinear feature extraction for pattern recognition. [via]

http://www.face-rec.org/algorithms/Kernel/ker...