A feature selection method using fixed-point algorithm for DNA microarray gene expression data

Authors:
Alok Sharma;Kuldip K. Paliwal;Seiya Imoto;Satoru Miyano;Vandana Sharma;Rajeshkannan Ananthanarayanan
Affiliations:
Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan and School of Engineering, Griffith University, Tokyo, Japan and School ...;School of Engineering, Griffith University, Tokyo, Japan;Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Fiji School of Medicine, University of the South Pacific, Tokyo, Japan;School of Engineering and Physics, University of the South Pacific, Tokyo, Japan
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2014

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Abstract

As the performance of hardware is limited, the focus has been to develop objective, optimized and computationally efficient algorithms for a given task. To this extent, fixed-point and approximate algorithms have been developed and successfully applied in many areas of research. In this paper we propose a feature selection method based on fixed-point algorithm and show its application in the field of human cancer classification using DNA microarray gene expression data. In the fixed-point algorithm, we utilize between-class scatter matrix to compute the leading eigenvector. This eigenvector has been used to select genes. In the computation of the eigenvector, the eigenvalue decomposition of the scatter matrix is not required which significantly reduces its computational complexity and memory requirement.