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《Journal of Shaoguan University》 2014-06
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K-Nearest-Neighbor-based L_(2,1) norm sparse regression classifier

XU Jie;ZHU Wen-kang;College of Mathematics and Information Science,Shaoguan University;  
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.
【Fund】: 国家自然科学基金资助项目(61305036)
【CateGory Index】: TP391.4
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