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《Journal of Software》 2008-11
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Local and Global Preserving Based Semi-Supervised Dimensionality Reduction Method

WEI Jia PENG Hong (School of Computer Science and Engineering,South China University of Technology,Guangzhou 510641,China)  
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.
【Fund】: Supported by the Natural Science Foundation of Guangdong Province of China under Grant No.07006474 (广东省自然科学基金);; the Sci & Tech Research Project of Guangdung Province of China under Grant No.2007B010200044 (广东省科技攻关项目)
【CateGory Index】: TP181
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【Co-references】
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