基于超松弛迭代的标签传播算法  

Label Propagation Algorithm Based on Over-Relaxation Iteration

作　　者：葛芳[1] 郭有强[1] 王年[2] 

机构地区：[1]蚌埠学院计算机科学与技术系,蚌埠233030 [2]安徽大学计算智能与信号处理教育部重点实验室,合肥230039

出　　处：《模式识别与人工智能》2016年第1期90-96,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.41001292);安徽省自然科学基金项目(No.11040606M151);蚌埠学院自然科学基金项目(No.2014ZR26)资助~~

摘　　要：针对标签传播算法中存在的问题,将超松弛迭代引入标签传播算法,解决标签序列的优化问题,提出基于超松弛迭代的标签传播算法(ORLP).该算法使用正负标签的方式标记已知样本,通过在近邻点间学习分类的方式预测未知样本的标签信息,同时在每次迭代时都能较好地保留初始标记点的标签信息,以指导下一次的标签传递过程.基于超松弛迭代推导ORLP的标签传播公式,同时证明标签序列的收敛性,得到标签序列的收敛解.实验表明,ORLP具有较高的分类准确率和较快的收敛速度.Aiming at the problem in the label propagation algorithm, over-relaxation iteration is introduced to solve the optimization problem of label sequence and an improved label propagation algorithm based on over-relaxation iteration （ORLP） is presented. The known samples are labeled with positive and negative labels and the label information of unknown samples is predicted by learning the classification between neighbor points. Meanwhile, the label information of initial labeled samples is reserved in each iteration to guide the next label propagation process. In addition, grounded on over-relaxation iteration, the label propagation formula of ORLP is inferred and the convergence of label sequence is proved simultaneously. Thus, the convergence solution of label sequence is obtained. The experimental results show that the ORLP has higher classification accuracy and convergence speed.

关 键 词：半监督学习 标签传播 超松弛迭代 标签序列 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]