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

Sharma, Alokanand and Paliwal, K.K. and Onwubolu, Godfrey C. (2006) Class-dependent PCA, MDC and LDA: a combined classifier for pattern classification. Pattern Recognition, 39 (7). pp. 1215-1229. ISSN 0031-3203

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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.

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: Ms Neha Harakh
Date Deposited: 15 Mar 2006 15:35
Last Modified: 14 May 2012 17:56
URI: http://repository.usp.ac.fj/id/eprint/4047
UNSPECIFIED

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