Rotational linear discriminant analysis using Bayes Rule for dimensionality reduction

Sharma, Alokanand and Paliwal, K.K. (2006) Rotational linear discriminant analysis using Bayes Rule for dimensionality reduction. Journal of Computer Science, 2 (9). pp. 754-757. ISSN 1549-3636

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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.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Alokanand Sharma
Date Deposited:	09 Aug 2006 23:48
Last Modified:	07 Oct 2013 04:04
URI:	http://repository.usp.ac.fj/id/eprint/5030

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