Linear discriminant analysis using rotational invariant L1 norm
نویسندگان
چکیده
منابع مشابه
Linear discriminant analysis using rotational invariant L1 norm
Linear 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 rotationa...
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Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotate...
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0167-8655/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2013.01.016 ⇑ Corresponding author. Tel.: +82 (0) 31 219 2480; E-mail addresses: [email protected] (J.H. Oh) @ieee.org (N. Kwak). 1 Jae Hyun Oh is pursuing a Ph.D. degree at the Computer Engineering, Ajou University, Republic of Ko 2 Nojun Kwak is an associate professor at the Depart Engineering, Ajou Universi...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2010
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2010.05.016