计算机技术与发展Issue(1)：66-69,73,5.DOI:10.3969/j.issn.1673-629X.2015.01.015
基于流形学习的正交稀疏保留投影鉴别分析
Orthogonal Sparsity Preserving Discriminant Analysis Based on Manifold Learning
摘要
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.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Key words
feature extraction/manifold learning/sparsity preserving projections/orthogonal/discriminant/terminating criterion分类
信息技术与安全科学引用本文复制引用
凌若冰,荆晓远,吴飞,姚永芳,李文倩..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,(1):66-69,73,5.基金项目
国家自然科学基金资助项目(61272273) （61272273）
江苏省普通高校研究生科研创新计划(CXLX13465) （CXLX13465）
