计算机技术与发展Issue(6)：63-66,4.DOI:10.3969/j.issn.1673-629X.2014.06.016
基于流形学习的整体正交稀疏保留鉴别分析
Analysis of Preserving Discriminant of Holistic Orthogonal Sparsity Based on Manifold Learning
摘要
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.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Key words
feature extraction/manifold learning/sparsity preserving projections/supervised learning/holistic orthogonal/face and palm-print image分类
信息技术与安全科学引用本文复制引用
吴飞,荆晓远,李文倩,姚永芳..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,(6):63-66,4.基金项目
国家自然科学基金资助项目(61073113,61272273) （61073113,61272273）
江苏省普通高校研究生科研创新计划(CXLX13_465) （CXLX13_465）
江苏省333工程(BRA2011175) （BRA2011175）
