Visualization of five erosion risk classes using Kernel discriminants. - Bartkowiak, A.; Evelpidou, N.; Vassilopoulos, A. (13th International Multi Conference ‘ACS Artificial Intelligence, Biometrics and Information Technology Security Computer Information Systems and Industrial Management Applications’, 2006)
Kernel discriminants are greatly appreciated because 1) they may express nonlinear boundaries between classes and 2) they permit to visualize graphically the data points belonging to di®erent classes. One such method is called GDA (Generalized Discriminant Analysis), however it operates on a kernel matrix of size NxN and is for large N prohibitive. We illustrate how this method works in a real situation when dealing with relatively large data. We consider a set of predictors of erosion risk in the Kefallinia island categorized into 5 classes of erosion risk (together N=3422 data items). We argue that a proper preparation of appropriate learning samples can greatly speed up the evaluations and result in good generalization properties. Our concern is to ¯nd appropriate data for learning. This is done by a kind of sieve algorithm.