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
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。