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

    Author(s)
    Sharma, A
    Paliwal, KK
    Imoto, S
    Miyano, S
    Sharma, V
    Ananthanarayanan, R
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2014
    Metadata
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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 ...
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    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.
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    Journal Title
    International Journal of Knowledge-Based and Intelligent Engineering Systems
    Volume
    18
    Issue
    1
    DOI
    https://doi.org/10.3233/KES-140285
    Subject
    Engineering
    Publication URI
    http://hdl.handle.net/10072/67289
    Collection
    • Journal articles

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