Kernel ridge regression classification

Abstract

We present a nearest nonlinear subspace classifier that extends ridge regression classification method to kernel version which is called Kernel Ridge Regression Classification (KRRC). Kernel method is usually considered effective in discovering the nonlinear structure of the data manifold. The basic idea of KRRC is to implicitly map the observed data into potentially much higher dimensional feature space by using kernel trick and perform ridge regression classification in feature space. In this new feature space, samples from a single-object class may lie on a linear subspace, such that a new test sample can be represented as a linear combination of class-specific galleries, then the minimum distance between the new test sample and class specific subspace is used for classification. Our experimental studies on synthetic data sets and some UCI benchmark datasets confirm the effectiveness of the proposed method.