Representation-based classification and recognition, such as face recognition, have dominant performance in dealing with high-dimension data. However, for low-dimension data the classification results are not satisfying. This paper proposes a classification method based on nearest neighbor representation in feature space, which extends representation-based classification to nonlinear feature space, and also remedies its drawback in low-dimension data processing. First of all, the proposed method projects the data into a high-dimension space through a kernel function. Then, the test sample is represented by the linear combination of all training samples and the corresponding coefficients of each training sample will be obtained. Finally, the test sample is assigned to the class of the training sample with a minimum distance. The results of experiments on standard two-class datasets and ORL and YALE face databases show that the algorithm has better classification performance.
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