计算机技术与发展Issue(7)：34-37,42,5.DOI:10.3969/j.issn.1673-629X.2014.07.009
基于流形学习的正交稀疏保留投影
Orthogonal Sparsity Preserving Projections Based on Manifold Learning
摘要
Abstract
Sparsity Preserving Projections ( SPP) extracts features by preserving the global sparse reconstruction relations among samples, which achieves favorable classification results. However,the obtained transformation of SPP usually is not orthogonal,while in real appli-cations,orthogonality is advantageous for classification in many scenarios. Besides,according to the manifold learning theory,local mani-fold structure is more important than global Euclidean structure. Therefore,in this paper,introduce manifold preserving and orthogonal transformation into SPP,and propose two novel approaches for face and palmprint image feature extraction,which are Holistic Orthogonal Manifold and Sparsity Preserving Projections ( HOMSPP) and Iterative Orthogonal Manifold and Sparsity Preserving Projections ( IOM-SPP) .关键词
人工智能/人脸和掌纹图像特征提取/流形学习/正交稀疏保留投影/子空间学习Key words
artificial intelligence/face and palmprint image feature extraction/manifold learning/orthogonal sparsity preserving projec-tions/subspace learning分类
信息技术与安全科学引用本文复制引用
刘茜,荆晓远,李文倩,姚永芳..基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,(7):34-37,42,5.基金项目
国家自然科学基金资助项目(61073113,61272273) （61073113,61272273）
江苏省333工程(BRA2011175) （BRA2011175）
