%0 Journal Article
%T Local and Global Preserving Based Semi-Supervised Dimensionality Reduction Method<br>基于局部与全局保持的半监督维数约减方法
%A WEI Jia
%A PENG Hong
%A <br>韦佳
%A 彭宏
%J 软件学报
%D 2008
%I 
%X In many machine learning and data mining tasks,it can't achieve the best semi-supervised learning result if only use side-information.So,a local and global preserving based semi-supervised dimensionality reduction (LGSSDR) method is proposed in this paper.LGSSDR algorithm can not only preserve the positive and negative constraints but also preserve the local and global structure of the whole data manifold in the low dimensional embedding subspace.Besides,the algorithm can compute the transformation matrix and handle unseen samples easily.Experimental results on several datasets demonstrate the effectiveness of this method.
%K side-information
%K local and global preserving
%K semi-supervised learning
%K dimensionality reduction
%K graph embedding<br>边信息
%K 局部与全局保持
%K 半监督学习
%K 维数约减
%K 图嵌入
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=ADB2C607E7B6A00B8FD7B3678BB9BDC1&yid=67289AFF6305E306&vid=2A8D03AD8076A2E3&iid=708DD6B15D2464E8&sid=2FD5A51E36BB2863&eid=F3006F85344912FD&journal_id=1000-9825&journal_name=软件学报&referenced_num=5&reference_num=25