A note on two-dimensional linear discriminant analysis

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

42 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2122-2128
Journal / PublicationPattern Recognition Letters
Volume29
Issue number16
Publication statusPublished - 1 Dec 2008

Abstract

2DLDA and its variants have attracted much attention from researchers recently due to the advantages over the singularity problem and the computational cost. In this paper, we further analyze the 2DLDA method and derive the upper bound of its criterion. Based on this upper bound, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem; on the other hand, we compare several distance measures when the feature matrices and feature vectors are applied. The matlab codes used in this paper are available at http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do?objectType=category&objectId=127&objectName=Application. © 2008 Elsevier B.V. All rights reserved.

Research Area(s)

  • 2DLDA, Discriminant power, Distance measure, Feature extraction, Linear discriminant analysis

Citation Format(s)

A note on two-dimensional linear discriminant analysis. / Liang, Zhizheng; Li, Youfu; Shi, Pengfei.
In: Pattern Recognition Letters, Vol. 29, No. 16, 01.12.2008, p. 2122-2128.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review