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VLSISP
2002

A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction

11 years 26 days ago
A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction
Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not all the newly added features are helpful to classification. Therefore it is necessary to reduce the dimensionality of feature space for effective and efficient pattern recognition. Two popular methods for dimensionality reduction are Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). While these methods are effective, there exists an inconsistency between feature extraction and the classification objective. In this paper we use Minimum Classification Error (MCE) training algorithm for feature dimensionality reduction and classification on Daterding and GLASS databases. The results of MCE training algorithms are compared with those of LDA and PCA.
Xuechuan Wang, Kuldip K. Paliwal
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where VLSISP
Authors Xuechuan Wang, Kuldip K. Paliwal
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