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Research of local approximation in semi-supervised manifold learning

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成果类型:
期刊论文
作者:
Yang, Gelan;Jin, Huixia;Yang, Gang
通讯作者:
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构:
[Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang 413000, China
[Yang, Gang] Department of Power and Energy Systems, Ecole Supé,rieur d'Electricit(Supé,lec), Gif-sur-Yvette Cedex, France
[Jin, Huixia] Department of Physics and Telecom Engineering, Hunan City University, Yiyang 413000, China
通讯机构:
Department of Computer Science, Hunan City University, China
语种:
英文
关键词:
Classification;Local approximation;Manifold learning;Semi-supervised learning
期刊:
The Journal of Information and Computational Science
ISSN:
1548-7741
年:
2010
卷:
7
期:
13
页码:
2681-2688
机构署名:
本校为第一且通讯机构
院系归属:
信息与电子工程学院
摘要:
In practical data classification and data mining, on one hand labeled data is required to agglomerate knowledge;on the other hand it may be too expensive to be obtained. Inspired by this dilemma, this paper introduced a scheme of local approximations based semi-supervised manifold learning. This method is a combination of the classical semi-supervised learning and the manifold learning, thus inherits their advantages in overcoming the dilemma by using a large number additional unlabeled data with labeled data and in dealing with complex high-dimensional data by using manifolds, respectively. F...

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