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2006

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

8 years 10 months ago
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification
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. 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Alok Sharma, Kuldip K. Paliwal, Godfrey C. Onwubol
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Alok Sharma, Kuldip K. Paliwal, Godfrey C. Onwubolu
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