Orthogonal locality minimizing globality maximizing projections for feature extraction

Feiping Nie; Shiming Xiang; Yangqiu SongChangshui Zhang

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Orthogonal locality minimizing globality maximizing projections for feature extraction

机译：Orthogonal locality minimizing globality maximizing projections for feature extraction

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摘要

Locality preserving projections (LPP) is a recently developed linear-feature extraction algorithm that has been frequently used in the task of face recognition and other applications. However, LPP does not satisfy the shift-invariance property, which should be satisfied by a linear-feature extraction algorithm. In this paper, we analyze the reason and derive the shift-invariant LPP algorithm. Based on the analysis of the geometrical meaning of the shift-invariant LPP algorithm, we propose two algorithms to minimize the locality and maximize the globality under an orthogonal projection matrix. Experimental results on face recognition are presented to demonstrate the effectiveness of the proposed algorithms.

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来源
《Optical Engineering》 |2009年第1期|017202-1-017202-5-0|共5页
作者
Feiping Nie; Shiming Xiang; Yangqiu SongChangshui Zhang;
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作者单位

Tsinghua University, Department of Automation, Beijing 100084, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
关键词
locality preserving projections; subspace learning; shift invariant; feature extraction; face recognition;

Orthogonal locality minimizing globality maximizing projections for feature extraction

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