Updates for Nonlinear Discriminants
Edin Andelic, Martin Schafföner, Marcel Katz, Sven E. Krüger, Andreas Wendemuth
A novel training algorithm for nonlinear discriminants for classification and regression in Reproducing Kernel Hilbert Spaces (RKHSs) is presented. It is shown how the overdetermined linear least-squares-problem in the corresponding RKHS may be solved within a greedy forward selection scheme by updating the pseudoinverse in an order-recursive way. The described construction of the pseudoinverse gives rise to an update of the orthogonal decomposition of the reduced Gram matrix in linear time. Regularization in the spirit of Ridge regression may then easily be applied in the orthogonal space. Various experiments for both classification and regression are performed to show the competitiveness of the proposed method.