Rotational Linear Discriminant Analysis Technique for Dimensionality Reduction

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Title Rotational Linear Discriminant Analysis Technique for Dimensionality Reduction
Author Sharma, Alok; Paliwal, Kuldip Kumar
Journal Name IEEE Transactions on Knowledge and Data Engineering
Editor Farokh B Bastani, Steve McConnell (Editor-in-Chief)
Year Published 2008
Place of publication United States
Publisher IEEE
Abstract The linear discriminant analysis (LDA) technique is very popular in pattern recognition for dimensionality reduction. It is a supervised learning technique that finds a linear transformation such that the overlap between the classes is minimum for the projected feature vectors in the reduced feature space. This overlap, if present, adversely affects the classification performance. In this paper, we introduce prior to dimensionality-reduction transformation an additional rotational transform that rotates the feature vectors in the original feature space around their respective class centroids in such a way that the overlap between the classes in the reduced feature space is further minimized. As a result, the classification performance significantly improves, which is demonstrated using several data corpuses.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69
Alternative URI http://dx.doi.org/10.1109/TKDE.2008.101
Copyright Statement Copyright 2008 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.
Volume 20
Issue Number 10
Page from 1336
Page to 1347
ISSN 1041-4347
Date Accessioned 2009-02-25
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/23591
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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