摘要
加权KNN(k-nearest neighbor)方法,仅利用了k个最近邻训练样本所提供的类别信息,而没考虑测试样本的贡献,因而常会导致一些误判。针对这个缺陷,提出了半监督KNN分类方法。该方法对序列样本和非序列样本,均能够较好地执行分类。在分类决策时,还考虑了c个最近邻测试样本的贡献,从而提高了分类的正确性。在Cohn-Kanade人脸库上,序列图像的识别率提高了5.95%,在CMU-AMP人脸库上,非序列图像的识别率提高了7.98%。实验结果表明,该方法执行效率高,分类效果好。
The category information of the k-nearest neighbor labeled samples is used, but the contribution of the test sam- ples is omitted in the weighted k-nearest neighbor method, which often lead to misclassifieations. 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.
出处
《中国图象图形学报》
CSCD
北大核心
2013年第2期195-200,共6页
Journal of Image and Graphics
基金
国家自然科学基金项目(60776834)
湖南省教育厅优秀青年项目(10B074)
关键词
加权KNN
贝叶斯理论
半监督KNN
流形
weighted k-nearest neighbor
Bayesian theory
semi-supervised k-nearest neighbor
manifold
