Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier

摘要

Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.