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《Application Research of Computers》 2011-04
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Weight local preserving based semi-supervised dimensionality reduction

WU Zheng-yin,XUE An-rong,LI Ming (School of Computer Science & Telecommunication Engineering,Jiangsu University,Zhenjiang Jiangsu 212013,China)  
The existing semi-supervised dimension reduction algorithms do not consider unlabeled information in the dataset,so they do not obtain the best result for dimension reduction.This paper presented an improved weight-based semi-supervised locality preserving dimensionality reduction algorithm.The algorithm not only maintained the positive and negative constraint information,but also kept the local structure of dataset using the distance weights.Experiments on UCI datasets show that the algorithm can improve the effect of dimensionality reduction,and also get better clustering results in the data distribution structure which does not meet the manifold.
【Fund】: 国家自然科学基金资助项目(60773049);; 江苏大学高级人才启动基金资助项目(09JDG041)
【CateGory Index】: TP181
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