Fast principal component analysis using fixed-point algorithm

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Title Fast principal component analysis using fixed-point algorithm
Author Sharma, Alokanand; Paliwal, Kuldip Kumar
Journal Name Pattern Recognition Letters
Year Published 2007
Place of publication Netherlands
Publisher Elsevier BV
Abstract In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/505619/description#description
Alternative URI http://dx.doi.org/10.1016/j.patrec.2007.01.012
Volume 28
Page from 1151
Page to 1155
ISSN 0167-8655
Date Accessioned 2008-01-24
Date Available 2009-09-21T05:51:11Z
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/10072/18520
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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