Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction

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Title Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction
Author Sharma, Alokanand; Paliwal, Kuldip Kumar
Journal Name Journal of Computer Science
Year Published 2006
Place of publication United States
Publisher Science Publications
Abstract Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotates the classes individually in the original feature space in a manner that enables further reduction of error. In this paper we present an extension of the rotational LDA technique by utilizing Bayes decision theory for class separation which improves the classification performance even further.
Peer Reviewed Yes
Published Yes
Alternative URI http://www.scipub.org/fulltext/jcs/jcs29754-757.pdf
Volume 2
Issue Number 9
Page from 754
Page to 757
ISSN 1549-3636
Date Accessioned 2007-03-18
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/14348
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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