CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Feature space locality constraint for kernel based nonlinear discriminant analysis
Lei, Zhen1; Mang, Zhiwei; Li, Stan Z.
Source PublicationPATTERN RECOGNITION
2012-07-01
Volume45Issue:7Pages:2733-2742
SubtypeArticle
AbstractSubspace 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.
KeywordLocality Constraint Feature Space Nonlinear Discriminant Analysis Face Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordFACE RECOGNITION ; DIMENSIONALITY REDUCTION ; ILLUMINATION ; FRAMEWORK ; POSE
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000302451000023
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7950
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.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
Recommended Citation
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.
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