Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22886
Title: A new LDA-KL combined method for feature extraction and its generalisation
Authors: Yang, J
Ye, H
Zhang, D 
Keywords: Feature fusion
Handwritten numeral recognition
K-L expansion, feature extraction
Linear discriminant analysis (LDA)
Issue Date: 2004
Source: Pattern analysis and applications, 2004, v. 7, no. 1, p. 40-50 How to cite?
Journal: Pattern Analysis and Applications 
Abstract: Linear discriminant analysis (LDA) is a well-known feature extraction technique. In this paper, we point out that LDA is not perfect because it only utilises the discriminatory information existing in the first-order statistical moments and ignores the information contained in the second-order statistical moments. We enhance LDA using the idea of a K-L expansion technique and develop a new LDA-KL combined method, which can make full use of both sections of discriminatory information. The proposed method is tested on the Concordia University CENPARMI handwritten numeral database. The experimental results indicate that the proposed LDA-KL method is more powerful than the existing techniques of LDA, K-L expansion and their combination: OLDA-PCA. What is more, the proposed method is further generalised to suit for feature extraction in the complex feature space and can be an effective tool for feature fusion.
URI: http://hdl.handle.net/10397/22886
ISSN: 1433-7541
DOI: 10.1007/s10044-004-0205-6
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