Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification

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Title Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification
Author Sharma, Alokanand; Paliwal, Kuldip Kumar; Onwubolu, Godfrey C.
Journal Name Pattern Recognition
Year Published 2006
Place of publication United Kingdom
Publisher Pergamon
Abstract Several pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers require very low storage requirement and processing time but their classification accuracy is not satisfactory. In either of the cases the performance of the classifier is poor. In this paper, we have presented a technique based on the combination of minimum distance classifier (MDC), class-dependent principal component analysis (PCA) and linear discriminant analysis (LDA) which gives improved performance as compared with other standard techniques when experimented on several machine learning corpuses.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#description
Alternative URI http://dx.doi.org/10.1016/j.patcog.2006.02.001
Volume 39
Page from 1215
Page to 1229
ISSN 0031-3203
Date Accessioned 2007-03-18
Date Available 2009-09-21T05:48:35Z
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/10072/14347
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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