Nearest Neighbor Convex Hull Classifier Based on Subspace Sample Selection
Jingyu Yang · Jisuanji gongcheng · 2008
Nearest Neighbor Convex Hull classifier(NNCH) involves solving convex quadratic programming problems,which require large memory and enormous time for large-scale problem.Therefore,it is important for NNCH to reduce the computation complexity and memory requirement without degrading the prediction accuracy.In this paper,a sample selection method named Subspace Sample Selection(SSS) algorithm is used to select a subset of data for NNCH.The SSS algorithm is a one-class iterative algorithm,which selects the furthest sample to the subspace of the chosen set in one class at each step.The experiments on the training-synthetic subset of MIT-CBCL face recognition database show that a significant amount of training samples can be removed,and the computation time of NNCH can be significantly reduced without any loss in accuracy.