基于扩散矩阵的半监督回归学习  

Semi-supervised learning for regression based on the diffusion matrix

作　　者：陈珩[1] 吴纪桃[1] 陈迪荣[1] 

机构地区：[1]北京航空航天大学数学与系统科学学院,北京100191

出　　处：《中国科学：数学》2014年第4期399-408,共10页Scientia Sinica：Mathematica

基　　金：国家自然科学基金(批准号:11171014和91130009);国家重点基础研究发展计划(973计划)(批准号:2010CB731900)资助项目

摘　　要：半监督学习算法用到标记和未标记的样本.大量的实验表明,利用无标记样本可以改进学习算法的逼近性能.然而,当样本数增加时,逼近性能的定量分析几乎没有.本文构造基于扩散矩阵的一种半监督学习算法,建立逼近阶.结果还量化地说明,未标记样本的使用可以减少逼近误差.Semi-supervised learning algorithms make use of labeled and unlabeled samples. A large number of experiments show that the use of unlabeled samples may improve approximation power. However, there is seldom quantitative analysis of approximation power when the number of unlabeled samples increases. In this paper a semi-supervised learning algorithm is constructed based on diffusion matrices. We establish the approximation order. Our results also illustrate quantitatively that the use of unlabeled samples may reduce the approximation error.

关 键 词：半监督学习 回归函数 扩散矩阵 

分 类 号：O151.21[理学—数学]