文章基本信息

标题：Collaborative Representation Classifier Based on K Nearest Neighbors for Classification
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作者：Jiangshu Wei ; Xiangjun Qi ; Mantao Wang 等
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2015
卷号：9
期号：1
页码：96-104
DOI：10.3923/jse.2015.96.104
出版社：Academic Journals Inc., USA
摘要：The Sparse Representation based Classifier (SRC) is a classical representation method for classification. The solution of SRC is obtained by l₁ norm minimization which cannot obtain the closed form solution. Thus, the computational complexity of SRC is a little high. The Collaborative Representation Classifier (CRC) is another classical method for classification. The solution of CRC is obtained by l₂ norm minimization, from the l₂ norm minimization, it can obtain the closed form solution which makes the computational complexity of CRC is much lower than SRC. Although, CRC is effective for classification, there are also some problems about CRC. Under some conditions, some test samples may be misclassified by CRC. This study proposes a local CRC method which is called KNN-CRC. This method firstly chooses K nearest neighbors of a test sample from all the training samples, then given a test sample, the test sample is represented by these K training samples. The solution of KNN-CRC is obtained by l₂ norm minimization and the size of K is much smaller than the total number of all training samples. Thus, the computational complexity of KNN-CRC is much lower than SRC and CRC. Furthermore, the extensive experiments show that the proposed KNN-CRC can obtain very competitive classification results compared with other methods.