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Local approximation in manifold learning

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成果类型:
期刊论文
作者:
Jin, Huixia;Tang, Jun;Yang, Gelan
通讯作者:
Jin, H.(Jinhuixia1980@163.com)
作者机构:
[Jin, Huixia] Department of Physics and Telecom Engineering, Hunan City University, Yiyang, China
[Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, China
[Tang, Jun] Department of Information Engineering, Hunan Urban Construction College, Xiangtan, China
语种:
英文
期刊:
Lecture Notes in Electrical Engineering
ISSN:
1876-1100
年:
2011
卷:
87 LNEE
期:
VOL. 2
页码:
603-610
机构署名:
本校为第一机构
院系归属:
信息与电子工程学院
摘要:
Both manifold learning and semi-supervised learning have been widely investigated in the past few years. Some of manifold learning algorithms, which are based on the idea of local approximation, can be used to control the way of transmitting information between point clouds. We combine local approximation with the idea of preserving projections and weighted integration, and give a set of solutions to semi-supervised regression and manifold alignment. Finally, we validated the effectiveness of the presented schemes in th...

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