Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62638
Title: A generalised K-L expansion method which can deal with small sample size and high-dimensional problems
Authors: Yang, J
Zhang, D 
Yang, JY
Keywords: Face recognition
Feature extraction
High dimensional problem
K-L expansion
Principal Component Analysis
Small sample size problem
Issue Date: 2003
Publisher: Springer
Source: Pattern analysis and applications, 2003, v. 6, no. 1, p. 47-54 How to cite?
Journal: Pattern analysis and applications 
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
URI: http://hdl.handle.net/10397/62638
ISSN: 1433-7541 (print)
1433-755X (online)
DOI: 10.1007/s10044-002-0177-3
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