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

Authors:
Alok Sharma;Kuldip K. Paliwal;Godfrey C. Onwubolu
Affiliations:
Signal Processing Lab, Griffith University, Brisbane, Australia;Signal Processing Lab, Griffith University, Brisbane, Australia;Department of Engineering, University of the South Pacific, Suva, Fiji
Venue:
Pattern Recognition
Year:
2006

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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.