Separable Two-Dimensional Linear Discriminant Analysis

Jianhua Zhao (), Philip L.H. Yu () and Shulan Li ()
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Jianhua Zhao: Yunnan University of Finance and Economics, School of Statistics and Mathematics
Philip L.H. Yu: The University of Hong Kong, Department of Statistics and Actuarial Science
Shulan Li: Yunnan University, Department of Mathematics and Statistics

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 597-604 from Springer

Abstract: Abstract Several two-dimensional linear discriminant analysis LDA (2DLDA) methods have received much attention in recent years. Among them, the 2DLDA, introduced by Ye, Janardan and Li (2005), is an important development. However, it is found that their proposed iterative algorithm does not guarantee convergence. In this paper, we assume a separable covariance matrix of 2D data and propose separable 2DLDA which can provide a neatly analytical solution similar to that for classical LDA. Empirical results on face recognition demonstrate the superiority of our proposed separable 2DLDA over 2DLDA in terms of classification accuracy and computational efficiency.

Keywords: LDA; 2DLDA; two-dimensional data; face recognition (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_62

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DOI: 10.1007/978-3-7908-2604-3_62

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