[1]吴飞,荆晓远,李文倩,等.基于流形学习的整体正交稀疏保留鉴别分析[J].计算机技术与发展,2014,24(06):63-66.
　WU Fei,JING Xiao-yuan,LI Wen-qian,et al.Analysis of Preserving Discriminant of Holistic Orthogonal Sparsity Based on Manifold Learning[J].,2014,24(06):63-66.
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基于流形学习的整体正交稀疏保留鉴别分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年06期
页码:
63-66
栏目:
智能、算法、系统工程
出版日期:
2014-06-30

文章信息/Info

Title:
Analysis of Preserving Discriminant of Holistic Orthogonal Sparsity Based on Manifold Learning
文章编号:
1673-629X（2014）06-0063-04
作者:
吴飞; 荆晓远; 李文倩; 姚永芳
南京邮电大学 自动化学院
Author(s):
WU Fei; JING Xiao-yuan; LI Wen-qian; YAO Yong-fang
关键词:
特征提取; 流形学习; 稀疏保留投影; 有监督学习; 整体正交; 人脸和掌纹图像
Keywords:
feature extraction; manifold learning; sparsity preserving projections; supervised learning; holistic orthogonal; face and palm-print image
分类号:
TP181
文献标志码:
A
摘要:
稀疏保留投影是一种有效的特征提取方法，但是其主要关注样本间的全局稀疏重构关系，并且得到的投影变换通常不是正交的。在实际应用中，图像数据往往处于高维空间中的一种低维流形中，正交性一直被认为有利于提高鉴别能力。文中以有监督学习的方式在稀疏保留投影中引入了流形结构保留，并使得投影空间正交，从而提出了一种新的特征提取方法，即基于流形学习的整体正交稀疏保留鉴别分析( MLHOSDA)。在人脸和掌纹图像数据库的实验结果表明此方法具有较好的识别效果。
Abstract:
Sparsity Preserving Projections ( SPP) is an effective feature extraction method. However,it focuses on the global sparse recon-struction relations among samples,and its achieved transformation is usually not orthogonal. In real application,image samples possibly reside on a nonlinear submanifold of the high-dimensional space,which is the inherent structure among the samples,and orthogonality is favorable for classification in many scenarios. In this paper,propose a new feature extraction approach named Manifold Learning based Holistic Orthogonal Sparsity preserving Discriminant Analysis ( MLHOSDA) ,which introduces the manifold preserving into SPP in a su-pervised learning manner and makes the obtained transformation orthogonal. The experiment results on face and palmprint image databases demonstrate the effectiveness of the proposed approach.

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更新日期/Last Update: 1900-01-01