计算机工程与应用2011，Vol.47Issue(21)：202-204,209,4.DOI:10.3778/j.issn.1002-8331.2011.21.053
基于测地距离的半监督增强
Semi-supervised boosting based on geodesic distance
摘要
Abstract
In many pattern recognition tasks,people often use the labeled samples.But the labeled sample may be time consuming to obtain, and sometimes human effort is needed.Then it is expensive to get while unlabeled data is much cheaper to obtain.Therefore, utilizing unlabeled data to boost the classifier has received a significant interest in pattern recognition in recent years.In semi-supervised learning, the unlabeled data is taken into account by the similarity between unlabeled data and labeled data.In the usual semi-boosting,people use the Euclidean distance to compute the similarity.However,the Euclidean distance only reflects the spatial relationship and ignores the manifold information.So this paper presents a semi-supervised boosting algorithm based on the geodesic distance,and then the manifold information in the sample space is reflected. The experimental results on the public data sets reveal that the proposed method can get encouraging recognition accuracy.关键词
测地距离/半监督学习/流形/增强Key words
geodesic distance/semi-supervised learning/manifold/boosting分类
信息技术与安全科学引用本文复制引用
刘志勇,袁媛..基于测地距离的半监督增强[J].计算机工程与应用,2011,47(21):202-204,209,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60975083). （the National Natural Science Foundation of China under Grant No.60975083）
