Feature space locality constraint for kernel based nonlinear discriminant analysis
文献类型：期刊论文
| 作者 | Lei, Zhen1 ; Mang, Zhiwei; Li, Stan Z.
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2012-07-01 |
| 卷号 | 45期号:7页码:2733-2742 |
| 关键词 | Locality constraint Feature space Nonlinear discriminant analysis Face recognition |
| 英文摘要 | Subspace learning is an important approach in pattern recognition. Nonlinear discriminant analysis (NDA), due to its capability of describing nonlinear manifold structure of samples, is considered to be more powerful to undertake classification tasks in image related problems. In kernel based NDA representation, there are three spaces involved, i.e., original data space, implicitly mapped high dimension feature space and the target low dimension subspace. Existing methods mainly focus on the information in original data space to find the most discriminant low dimension subspace. The implicit high dimension feature space plays a role that connects the original space and the target subspace to realize the nonlinear dimension reduction, but the sample geometric structure information in feature space is not involved. In this work, we try to utilize and explore this information. Specifically, the locality information of samples in feature space is modeled and integrated into the traditional kernel based NDA methods. In this way, both the sample distributions in original data space and the mapped high dimension feature space are modeled and more information is expected to be explored to improve the discriminative ability of the subspace. Two algorithms, named FSLC-KDA and FSLC-KSR. are presented. Extensive experiments on ORL, Extended-YaleB, PIE, Multi-PIE and FRGC databases validate the efficacy of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | FACE RECOGNITION ; DIMENSIONALITY REDUCTION ; ILLUMINATION ; FRAMEWORK ; POSE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000302451000023 |
| 公开日期 | 2015-09-22 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/7950]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lei, Zhen,Mang, Zhiwei,Li, Stan Z.. Feature space locality constraint for kernel based nonlinear discriminant analysis[J]. PATTERN RECOGNITION,2012,45(7):2733-2742. |
| APA | Lei, Zhen,Mang, Zhiwei,&Li, Stan Z..(2012).Feature space locality constraint for kernel based nonlinear discriminant analysis.PATTERN RECOGNITION,45(7),2733-2742. |
| MLA | Lei, Zhen,et al."Feature space locality constraint for kernel based nonlinear discriminant analysis".PATTERN RECOGNITION 45.7(2012):2733-2742. |
入库方式： OAI收割
来源：自动化研究所
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