Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1187
Title: Unsupervised discriminant projection analysis for feature extraction
Authors: Yang, J
Zhang, DD 
Jin, Z
Yang, JY
Keywords: Database systems
Feature extraction
Linear programming
Principal component analysis
Problem solving
Issue Date: 2006
Publisher: IEEE Computer Society
Source: The 18th International Conference on Pattern Recognition : 20-24 August, 2006, Hong Kong : proceedings, v. 1, p. 904-907 How to cite?
Abstract: This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method - Locality preserving projection (LPP, which considers the local information only) for classification tasks. The proposed method is applied to face biometrics and examined using the ORL and FERET face image databases. Our experimental results show that UDP consistently outperforms LPP, PCA, and LDA.
URI: http://hdl.handle.net/10397/1187
ISBN: 0-7695-2521-0
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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