关键词: 测地距离, 半监督学习, 流形, 增强

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.

Key words: geodesic distance, semi-supervised learning, manifold, boosting