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. Copyright © 2010 Springer-Verlag Berlin Heidelberg.
|Title of host publication||Proceedings of COMPSTAT'2010: 19th International Conference on Computational Statistics Paris France, August 22-27, 2010 keynote, invited and contributed papers|
|Editors||Yves LECHEVALLIER, Gilbert SAPORTA|
|Place of Publication||Heidelberg|
|Publisher||Springer-Verlag Berlin Heidelberg|
|Publication status||Published - 2010|