版权说明 操作指南
首页 > 成果 > 详情

Research of high dimensional data corresponding based on semi supervised manifold learning

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Yang, Gelan;He, Qian;Deng, Xiaojun
通讯作者:
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构:
[He, Qian; Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, 413000, China
[Deng, Xiaojun] College of Computer and Communication, Hunan University of Technology, Zhuzhou, 412008, China
[He, Qian] College of Mathematics and Econometric, Hunan University, Changsha, 410000, China
通讯机构:
[Yang, G.] D
Department of Computer Science, , Yiyang, 413000, China
语种:
英文
关键词:
Data analysis;Image manifolds;Manifold learning
期刊:
International Journal of Advancements in Computing Technology
ISSN:
2005-8039
年:
2011
卷:
3
期:
10
页码:
266-273
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:
Data correspondences and data matching are important tasks of high-dimensional data analysis. In this paper, we discuss a family of semi-supervised learning algorithms for studying different manifold data sets corresponding. The algorithm improves local embedding or density models by elevating their status to full global dimensionality reduction without requiring any modification to their training procedures or cost functions. By using self correspondence between examples in the same data set, the method can improve the performance of these data sets with limited numbers of examples. The effec...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com