Kernel exploratory projection pursuit
Kernel methods are a recent innovation allowing one to perform efficient linear operations in a nonlinear space with the net effect of having nonlinear operations in data space. We derive three different methods of performing exploratory projection pursuit in kernel space and show on a standard data set that each gives interesting but different projections.