[1]凌若冰[],荆晓远[],吴飞[],等. 基于流形学习的正交稀疏保留投影鉴别分析[J].计算机技术与发展,2015,25(01):66-69.
　LING Ruo-bing[],JING Xiao-yuan[],WU Fei[],et al. Orthogonal Sparsity Preserving Discriminant Analysis Based on Manifold Learning[J].,2015,25(01):66-69.
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 基于流形学习的正交稀疏保留投影鉴别分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年01期
页码:
66-69
栏目:
智能、算法、系统工程
出版日期:
2015-01-10

文章信息/Info

Title:
 Orthogonal Sparsity Preserving Discriminant Analysis Based on Manifold Learning
文章编号:
1673-629X（2015)01-0066-04
作者:
 凌若冰[1];  荆晓远[1] ; 吴飞[2] ; 姚永芳[1] ; 李文倩[1]
 1.南京邮电大学 自动化学院；2.南京邮电大学 通信与信息工程学院
Author(s):
 LING Ruo-bing[1] ; JING Xiao-yuan[1];  WU Fei[2] ; YAO Yong-fang[1];  LI Wen-qian[1]
关键词:
 ; 特征提取; 流形学习; 稀疏保留投影; 正交; 鉴别; 终止准则
Keywords:
 ; feature extraction; manifold learning; sparsity preserving projections; orthogonal; discriminant; terminating criterion
分类号:
TP301
文献标志码:
A
摘要:
 稀疏保留投影( SPP)是一种保留样本间的稀疏重构关系的特征提取方法。但是根据流形学习理论,考虑局部流形结构比考虑全局欧氏结构更重要。此外,SPP得到的不是一组正交的投影向量,特征间存在冗余信息。为解决该问题,文中提出一种改进的稀疏保留投影算法,在SPP中引入有监督的流形学习,使得所得投影空间正交,并用迭代的方式求解最优投影变换,称为基于流形学习的迭代正交稀疏保留鉴别分析( MLIOSDA)。同时提出一种终止准则终止迭代。在CAS-PEAL人脸数据库和PolyU掌纹数据库的实验结果表明,文中提出的方法与一些相关方法相比有效地提高了识别结果。
Abstract:
 Sparsity Preserving Projections ( SPP) is an effective feature extraction method,which can preserve the sparse reconstruction re-lations among samples. However,according to the manifold learning theory,the local manifold structure of samples is more important than the global Euclidean structure of samples. SPP cannot get a set of orthogonal projection vectors,and thus there exists redundant informa-tion among the obtained features. To address these problems of SPP,propose a novel approach called Manifold Learning based Iterative Orthogonal Sparsity preserving Discriminant Analysis ( MLIOSDA) ,which introduces the idea of manifold learning into SPP and obtains orthogonal projection space. Obtain optimal projection vectors in an iterative manner. Also provide a terminating criterion to finish the it-eration. Experimental results on CAS-PEAL and PolyU databases demonstrate that the proposed approach can effectively improve the rec-ognition results compared with some related methods.

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更新日期/Last Update: 2015-04-17