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, Jian
Zhang, David D.
Jin, Zhong
Yang, Jing-yu
Subjects: 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.
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.
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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1187
ISBN: 0-7695-2521-0
Appears in Collections:COMP Conference Papers & Presentations

Files in This Item:
File Description SizeFormat 
unsupervised-discriminant_06.pdf126.26 kBAdobe PDFView/Open


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.