Semi-supervised learning via manifold regularization  

作　　者：MAO Yu ZHOU Yan-quan LI Rui-fan WANG Xiao-jie ZHONG Yi-xin 

机构地区：[1]School of Telecommunication Engineering,Beijing University of Posts and Telecommunications

出　　处：《The Journal of China Universities of Posts and Telecommunications》2012年第6期79-88,共10页中国邮电高校学报（英文版）

基　　金：supported by the Mechanism Socialist Method and Higher Intelligence Theory of the National Natural Science Fund Projects(60873001)

摘　　要：This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

关 键 词：manifold regularization semi-supervised learning transductive learning expectation maximization algorithm CLASSIFICATION text categorization 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]