中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A General Exponential Framework for Dimensionality Reduction

文献类型:期刊论文

作者Wang, Su-Jing1,2; Yan, Shuicheng3; Yang, Jian4; Zhou, Chun-Guang2; Fu, Xiaolan1; Fu,Xiaolan
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2014-02-01
卷号23期号:2页码:920-930
关键词Face recognition manifold learning matrix exponential Laplacian embedding dimensionality reduction
ISSN号1057-7149
英文摘要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.
收录类别SCI
语种英语
WOS记录号WOS:000329581800034
源URL[http://ir.psych.ac.cn/handle/311026/14174]  
专题心理研究所_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
推荐引用方式
GB/T 7714
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,&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.

入库方式: OAI收割

来源:心理研究所

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