Linear boundary discriminant analysis based on QR decomposition

Linear boundary discriminant analysis (LBDA) shows good feature extraction performance in the classification problem. However, LBDA suffers from small sample size (SSS) problem and the computation time of it increases exponentially for datasets that are not sufficiently large compared with the numbe...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2014-02, Vol.17 (1), p.105-112
Main Authors: Na, Jin Hee, Park, Myoung Soo, Kang, Woo-Sung, Choi, Jin Young
Format: Article
Language:English
Subjects:
Applied sciences
Computer Science
Computer science
control theory
systems
Data processing. List processing. Character string processing
Exact sciences and technology
Memory organisation. Data processing
Pattern Recognition
Software
Theoretical Advances
Citations: Items that this one cites
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Summary:Linear boundary discriminant analysis (LBDA) shows good feature extraction performance in the classification problem. However, LBDA suffers from small sample size (SSS) problem and the computation time of it increases exponentially for datasets that are not sufficiently large compared with the number of features. To release these problems, we reformulate LBDA using QR decomposition, and this results in both reducing computation time and resolving SSS problem while classification performance is maintained.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-012-0285-7