A self training semi-supervised truncated kernel projection machine for link prediction

摘要：

　　With the large amount of complex network data becoming available in the web,link prediction has become a popular research field of data mining.We focus on the link prediction task which can be formulated as a binary classification problem in social network.To treat this problem,a sparse semi-supervised classification algorithm called Self Training Semi-supervised Truncated Kernel Projection Machine (STKPM),based on empirical feature selection,is proposed for link prediction.Experimental results show that the proposed algorithm outperformed several outstanding learning algorithms with smaller test errors and more stability.

关键词： Self training semi-supervised truancated kernel projection machine Link prediction Social network

作者: Liang Huang Ruixuan Li Kunmei Wen Xiwu GU

作者单位: School of Computer Science and Technology, Huazhong University of Science and Technology,Wuhan 430074, China

会议类型: 国际会议

会议名称: 2012 International Mechanical Properties and Structural Materials Conference(IMPSMC2012)(2012机械性能与结构材料国际会议)

会议地点: 太原

会议语种:英文

页码: 369-373

在线出版日期: 2012-08-17（万方平台首次上网日期，不代表论文的发表时间）