Two-way Markov random walk transductive learning algorithm  

作　　者：李宏 卢小燕 刘玮文 Clement K.Kirui 

机构地区：[1]School of Information Science and Engineering, Central South University [2]Department of Electronic and Information Engineering, Huazhong University of Science and Technology

出　　处：《Journal of Central South University》2014年第3期970-977,共8页中南大学学报（英文版）

基　　金：Project(61232001) supported by National Natural Science Foundation of China;Project supported by the Construct Program of the Key Discipline in Hunan Province,China

摘　　要：Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk （TMRW） algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.

关 键 词：CLASSIFICATION transductive learning two-way Markov random walk （TMRW） Adboost.MH 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V241.558[自动化与计算机技术—控制科学与工程]