Abstract
The K-L expansion method, which is able to extract the discriminatory information contained in class-mean vectors, is generalised, in this paper, to make it suitable for solving small sample size problems. We further investigate, theoretically, how to reduce the method’s computational complexity in high-dimensional cases. As a result, a simple and efficient GKLE algorithm is developed. We test our method on the ORL face image database and the NUST603 handwritten Chinese character database, and our experimental results demonstrate that GKLE outperforms the existing techniques of PCA, PCA plus LDA, and Direct LDA.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
ID="A1"Correspondance and offprint requests to: J-Y. Yang, Department of Computer Science, Nanjing University of Science and Technology, Nanjiung 210094, P.R. China. E-mail: csiyang@comp.poly.edu.hk
Rights and permissions
About this article
Cite this article
Yang, J., Zhang, D. & Yang, JY. A generalised K-L expansion method which can deal with small sample size and high-dimensional problems. Pattern Anal Appl 6, 47–54 (2003). https://doi.org/10.1007/s10044-002-0177-3
Issue Date:
DOI: https://doi.org/10.1007/s10044-002-0177-3