Nonlinear Nearest Subspace Classifier

Abstract

As an effective nonparametric classifier, nearest subspace (NS) classifier exhibits its good performance on high-dimensionality data. However, NS could not well classify the data with the same direction distribution. To deal with this problem, this paper proposes a nonlinear extension of NS, or nonlinear nearest subspace classifier. Firstly, the data in the original sample space are mapped into a kernel empirical mapping space by using a kernel empirical mapping function. In this kernel empirical mapping space, NS is then performed on these mapped data. Experimental results on the toy and face data show this nonlinear nearest subspace classifier is a promising nonparametric classifier. © 2011 Springer-Verlag.