A note on two-dimensional linear discriminant analysis
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 2122-2128 |
Journal / Publication | Pattern Recognition Letters |
Volume | 29 |
Issue number | 16 |
Publication status | Published - 1 Dec 2008 |
Link(s)
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.
In: Pattern Recognition Letters, Vol. 29, No. 16, 01.12.2008, p. 2122-2128.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review