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A General Exponential Framework for Dimensionality Reduction
Wang, Su-Jing1,2; Yan, Shuicheng3; Yang, Jian4; Zhou, Chun-Guang2; Fu, Xiaolan1
2014-02-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
文章类型Article
卷号23期号:2页码:920-930
摘要As a general framework, Laplacian embedding, based on a pairwise similarity matrix, infers low dimensional representations from high dimensional data. However, it generally suffers from three issues: 1) algorithmic performance is sensitive to the size of neighbors; 2) the algorithm encounters the well known small sample size (SSS) problem; and 3) the algorithm de-emphasizes small distance pairs. To address these issues, here we propose exponential embedding using matrix exponential and provide a general framework for dimensionality reduction. In the framework, the matrix exponential can be roughly interpreted by the random walk over the feature similarity matrix, and thus is more robust. The positive definite property of matrix exponential deals with the SSS problem. The behavior of the decay function of exponential embedding is more significant in emphasizing small distance pairs. Under this framework, we apply matrix exponential to extend many popular Laplacian embedding algorithms, e. g., locality preserving projections, unsupervised discriminant projections, and marginal fisher analysis. Experiments conducted on the synthesized data, UCI, and the Georgia Tech face database show that the proposed new framework can well address the issues mentioned above.
关键词Face recognition manifold learning matrix exponential Laplacian embedding dimensionality reduction
收录类别SCI
语种英语
WOS记录号WOS:000329581800034
引用统计
被引频次:26[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/14174
专题脑与认知科学国家重点实验室
作者单位1.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
2.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
3.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119077, Singapore
4.Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
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Wang, Su-Jing,Yan, Shuicheng,Yang, Jian,et al. A General Exponential Framework for Dimensionality Reduction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):920-930.
APA Wang, Su-Jing,Yan, Shuicheng,Yang, Jian,Zhou, Chun-Guang,&Fu, Xiaolan.(2014).A General Exponential Framework for Dimensionality Reduction.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),920-930.
MLA Wang, Su-Jing,et al."A General Exponential Framework for Dimensionality Reduction".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):920-930.
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