CAJ | 학술논문

在模式分类中．基于旋转不变范数的回归分类器（RRC）最近得到广泛的应用．然而RRC的稀疏重构是建立在全体训练样本之上．当训练样本的数量很大时，计算的时耗比较大．同时，对稀疏程度的过度追求也在一定程度上影响了分类性能．基于K最近邻分类器提出了一类局部的基于K最近邻的L2,1范数稀疏回归分类器（KNN—SRC），该分类器比全局的RRC计算速度快，同时。利用K最近邻点代替全体训练样本，在一定程上避免了非同类的相似样本对测试样本的过度稀疏表示，从而提高分类性能．KNN—SRC的分类性能在UCI的Wine数据集和Yale人脸数据库上作了检测．测试结果表明KNN—SRC分类性能优于RRC．
재모식분류중．기우선전불변범수적회귀분류기（RRC）최근득도엄범적응용．연이RRC적희소중구시건립재전체훈련양본지상．당훈련양본적수량흔대시，계산적시모비교대．동시，대희소정도적과도추구야재일정정도상영향료분류성능．기우K최근린분류기제출료일류국부적기우K최근린적L2,1범수희소회귀분류기（KNN—SRC），해분류기비전국적RRC계산속도쾌，동시。이용K최근린점대체전체훈련양본，재일정정상피면료비동류적상사양본대측시양본적과도희소표시，종이제고분류성능．KNN—SRC적분류성능재UCI적Wine수거집화Yale인검수거고상작료검측．측시결과표명KNN—SRC분류성능우우RRC．
The Rotational-invariant-norm-based Regression for Classification （RRC） has been developed and shows great potential for pattern classification. RRC is a global representation based method in that a testing sample is represented by all training samples. Thus, on the one hand, it is time-consuming when the number of training samples is large; on the other hand, with the extremely sparse reconstructive coefficients, RRC sometimes will lead to misclassifications. This paper presents a local RRC method, called KNN-SRC, which chooses K nearest neighbors of a testing sample from all training sample to represent the testing sample. Since K is much smaller compared to the total number of training samples, KNN-SRC is much faster than the global RRC. More importantly, when there exists a class formed by parts of objects among many classes of objects, taking the K nearest neighbors as the training samples can avoid the misclassification. The proposed KNN-SRC is tested using the UCI Wine dataset and the Yale face database. The experimental results show KNN-SRC is more effective and efficient than RRC and other competitive methods.