关键词: 样本选择, 子空间, 支持向量机, 人脸识别, 模式分类

Abstract: Sample selection is an important topic for SVM.To attack it，a novel intra-class method based on subspace approximation of training class dataset is proposed in this paper.In one class，the subspace of the chosen set is used to approximate that of the original set.An iterative algorithm is employed to realize this process.The furthest sample to the subspace of the chosen set is selected at each step.The comparative experiments on the training-synthetic set of the MIT-CBCL face recognition database show that much lower selection ratio，much less sampling time and much faster test speed has been obtained by this approach combined with linear SVM without a loss of accuracy.

Key words: sample selection, subspace, Support Vector Machine（SVM）, face recognition, pattern classification