Improved direct LDA and its application to DNA microarray gene expression data

Authors:
Kuldip K. Paliwal;Alok Sharma
Affiliations:
School of Engineering, Griffith University, QLD-4111, Australia;Signal Processing Lab, Griffith University, QLD-4111, Australia and School of Engineering and Physics, University of the South Pacific, Suva, Fiji
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

The direct linear discriminant analysis (DLDA) technique is a well known technique for dimensionality reduction. It can overcome the small sample size problem. However, its performance is limited. In this paper we address its drawbacks and propose an improvement of the DLDA technique. The experiment is conducted on several DNA microarray gene expression datasets and the performance (in terms of classification accuracy) of the proposed improvement of the technique is reported at 91.1% which is very promising.