[1]文贵华,江丽君,文 军.局部测地距离估计的Hessian局部线性嵌入[J].智能系统学报,2008,3(05):429-435.
 WEN Gui-hua,J IANG L i-jun,WEN Jun.Using locally estimated geodesic distances to improve Hessian local linear embedding[J].CAAI Transactions on Intelligent Systems,2008,3(05):429-435.
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局部测地距离估计的Hessian局部线性嵌入(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第3卷
期数:
2008年05期
页码:
429-435
栏目:
出版日期:
2008-10-25

文章信息/Info

Title:
Using locally estimated geodesic distances to improve Hessian local linear embedding
文章编号:
1673-4785 (2008) 05-0429-07
作者:
文贵华1 江丽君2 文 军3
1. 华南理工大学计算机科学与工程学院, 广东广州510641;
2. 华南理工大学电子材料科学与工程系,广东广州 510641;
3. 湖北民族学院理学院,湖北恩施445000
Author(s):
WEN Gui-hua1 J IANG L i-jun2 WEN Jun3
1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China;
2. Department of ElectronicMaterial Science and Engineering, South China University of Technology, Gunagzhou 510641, China;
 3. School ofMathe2 matical Science, Hubei Insitute forNationalities, Enshi 445000, China
关键词:
流形学习 Hessian变换局部线性嵌入 测地距离
Keywords:
manifold learning Hessian transformation locally linear embedding geodesic distance
分类号:
TP391
文献标志码:
A
摘要:
为处理极度弯曲的数据流形,提出了基于局部测地距离估计的Hessian局部线性嵌入算法. 算法采用Hessian局部线性嵌入(HLLE)的概念框架,采用局部估计的测地距离而不是欧氏距离来确定每个点的邻域,从而减少数据流形弯曲对邻域选择的影响. 算法可认为是全局和局部方法的综合,在性能上不仅比HLLE显著提高,有更强的鲁棒性,而且时间增加不明显. 标准数据集上的实验结果验证了所提方法的有效性.
Abstract:
To dealwith highly curved data manifolds, the Hessian locally linear embedding (HLLE) algorithm was modified based on a locally estimated geodesic distance. It used the general concep tual framework of HLLE to guar2 antee correct setting of local isometry to an open connected subset. It emp loys the locally estimated geodesic dis2 tance instead of the Euclidean distance to determine the neighborhood of any point, so that it reduces the distorting influence of curvature of the data manifold on determining the neighborhood. This app roach can be regarded as the integration of a localmethod and a globalmethod, so that it has better performance and stability than HLLE, with only a slight increase in computational time. Experiments conducted on benchmark data sets verified etficioncy of the p roposed app roach.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2007-11-25.
基金项目:广东省科技攻关资助项目( 2007B030803006) ; 湖北省科技攻关资助项目(2005AA101C17)
作者简介:
文贵华, 男, 1968 年生,主要研究方向为创新计算、认知几何、机器学习与数据挖掘.
江丽君, 女, 1971 年生,主要研究方向为机器学习与图形CAD.
文 军, 男, 1964 年生,主要研究方向为创新计算与应用.
通信作者:文贵华. E-mail: crghwen@scut. edu. cn.
更新日期/Last Update: 2009-05-18