%0 Journal Article
%T Semi-supervised k-nearest neighbor classification method<br>半监督k近邻分类方法
%A Chen Rixing
%A Zhu Minghan
%A <br>陈日新
%A 朱明旱
%J 中国图象图形学报
%D 2013
%I 
%X The category information of the k-nearest neighbor labeled samples is used, but the contribution of the test samples is omitted in the weighted k-nearest neighbor method, which often lead to misclassifications. Aimed at the problem, a semi-supervised k-nearest neighbor method is proposed in this paper. The method can classify sequential samples and non-sequential samples better than the k-nearest neighbor method. In the decision process of classification, the information of c-nearest neighbor samples in the test set is used. So, classification accuracy is improved. The recognition accuracy of the method is 5.95% higher for sequential images in Cohn-Kanade face database, and 7.89% higher for non-sequential images in Cohn-Kanade face database than it of weighted k-nearest neighbor method. The experiment shows that the method performs fast and has high classification accuracy.
%K weighted k-nearest neighbor
%K Bayesian theory
%K semi-supervised k-nearest neighbor
%K manifold<br>加权KNN
%K 贝叶斯理论
%K 半监督KNN
%K 流形
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=AE8DAD4245F3E9A93F66030905EBA6ED&yid=FF7AA908D58E97FA&vid=13553B2D12F347E8&iid=0B39A22176CE99FB&sid=64963996248CBF47&eid=1E41DF9426604740&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=19