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Linear discriminant analysis using rotational invariant L-1 norm
Li, Xi1,4; Hu, Weiming1; Wang, Hanzi2; Zhang, Zhongfei3
Source PublicationNEUROCOMPUTING
2010-08-01
Volume73Issue:13-15Pages:2571-2579
SubtypeArticle
AbstractLinear discriminant analysis (LDA) is a well-known scheme for supervised subspace learning. It has been widely used in the applications of computer vision and pattern recognition. However, an intrinsic limitation of LDA is the sensitivity to the presence of outliers, due to using the Frobenius norm to measure the inter-class and intra-class distances. In this paper, we propose a novel rotational invariant L-1 norm (i.e., R-1 norm) based discriminant criterion (referred to as DCL1), which better characterizes the intra-class compactness and the inter-class separability by using the rotational invariant L-1 norm instead of the Frobenius norm. Based on the DCL1, three subspace learning algorithms (i.e., 1DL(1), 2DL(1), and TDL1) are developed for vector-based, matrix-based, and tensor-based representations of data, respectively. They are capable of reducing the influence of outliers substantially, resulting in a robust classification. Theoretical analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed DCL1 and its algorithms. (C) 2010 Elsevier B.V. All rights reserved.
KeywordLinear Discriminant Analysis Face Classification R-1 Norm
WOS HeadingsScience & Technology ; Technology
WOS KeywordFACE RECOGNITION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000281612300030
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9728
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Adelaide, Dept Comp Sci, Adelaide, SA 5005, Australia
3.SUNY Binghamton, Binghamton, NY 13902 USA
4.TELECOM ParisTech, CNRS, Paris, France
Recommended Citation
GB/T 7714
Li, Xi,Hu, Weiming,Wang, Hanzi,et al. Linear discriminant analysis using rotational invariant L-1 norm[J]. NEUROCOMPUTING,2010,73(13-15):2571-2579.
APA Li, Xi,Hu, Weiming,Wang, Hanzi,&Zhang, Zhongfei.(2010).Linear discriminant analysis using rotational invariant L-1 norm.NEUROCOMPUTING,73(13-15),2571-2579.
MLA Li, Xi,et al."Linear discriminant analysis using rotational invariant L-1 norm".NEUROCOMPUTING 73.13-15(2010):2571-2579.
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