Kernel Affine Subspace Nearest Points Classification Algorithm

A novel pattern recognition algorithm called Kernel Affine Subspace Nearest Points(KASNP) classification is presented.Inspired by the geometrical explanation of Support Vector Machine(SVM) that the optimal separating plane bisects the closest points within two class convex hulls,KASNP algorithm expands the searching areas of the closest points from the convex hulls to their corresponding class affine subspaces in kernel space.The affine subspaces are taken as the rough estimations of the class feature sample distributions,and their closest points are found.The hyperplane to separate the affine subspaces with the maximal margins is constructed,which is the perpendicular bisector of the line segment joining the two closest points.The test experiments compared with the Nearest Neighbor(1-NN) classifier and SVM on the ORL face recognition database show good performance of this algorithm.