Affiliations: [a] Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan | [b] School of Engineering, Griffith University, Tokyo, Japan | [c] School of Engineering and Physics, University of the South Pacific, Tokyo, Japan | [d] Fiji School of Medicine, University of the South Pacific, Tokyo, Japan
Correspondence:
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Corresponding author: Alok Sharma, Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. Tel.: +81 3 5449 5615; Fax: +81 3 5449 5442; E-mail: [email protected]
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.
Keywords: Feature selection, fixed-point algorithm, DNA microarray gene expression data, fast PCA
