Institutional Repository, Institute of Psychology, Chinese Academy of Sciences
| A General Exponential Framework for Dimensionality Reduction | |
Wang,Su-Jing1,2; Yan,Shuicheng3; Yang,Jian4; Zhou,Chun-Guang2; Fu,Xiaolan1
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| 第一作者 | Wang, Su-Jing |
| 通讯作者邮箱 | [email protected] ; [email protected] ; [email protected] ; [email protected] ; [email protected] |
| 心理所单位排序 | 1 |
| 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 |
| 学科领域 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 2014-02-01 | |
| 语种 | 英语 |
| DOI | 10.1109/TIP.2013.2297020 |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| ISSN | 1057-7149 |
| 卷号 | 23期号:2页码:920-930 |
| 期刊论文类型 | Article |
| URL | 查看原文 |
| 收录类别 | SCI |
| WOS关键词 | LINEAR DISCRIMINANT-ANALYSIS ; PRESERVING PROJECTIONS ; FACE ; MATRIX ; EIGENFACES ; ALGORITHM ; COMPUTE |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000329581800034 |
| WOS分区 | Q1 |
| 引用统计 | |
| 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 |
| 第一作者单位 | 脑与认知科学国家重点实验室 |
| 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.(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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| WOS000329581800034.p（6755KB） | 期刊论文 | 出版稿 | 暂不开放 | CC BY-NC-SA | 请求全文 | |
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