Kernel based subspace pattern classification

In this paper we introduce a new classifier, K-CLAFIC (Kernel based extension of CLAss Featuring Information Compression). CLAFIC is a statistical classification paradigm which associates with each output class a linear subspace. Thus patterns are classified based on their distance from different vector subspaces. Based on a newly introduced method to perform nonlinear principal component analysis, we present K-CLAFIC as a natural nonlinear extension of CLAFIC. Thus in K-CLAFIC there is a nonlinear subspace associated with each class and patterns are classified based on their distance from different nonlinear subspaces. K-CLAFIC is simple in operation and gives highly competitive performance on standard datasets. Also, since there are no iterative procedures for parameter optimization, in spite of being a nonlinear classifier, it is fast in operation