Kernel Fisher Discriminant Analysis

This method is an implementation of the Kernel Fisher Discriminant (KFD) [Mika99a]. The aim is to find a binary kernel classifier which is the linear decision function in a feature space induced by the selected kernel function.

Kernel Principal Component Analysis

Kernel Principal Component Analysis (Kernel PCA) [5,6] is a method of non-linear feature extraction, closely related to methods applied in Support Vector Machines (SVMs)[3].
A cite of code of KPCA can be seen as follows:
http://cmp.felk.cvut.cz/cmp/software/stprtool/manual/kernels/extraction/list/kpca.html

Another photo

A photo in the Three Gorges by me