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  • A deterministic approach to regularized linear discriminant analysis

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    Accepted Manuscript (AM)
    Author(s)
    Sharma, Alok
    Paliwal, Kuldip K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2015
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    Abstract
    The regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and ...
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    The regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and favorable results are obtained.
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    Journal Title
    Neurocomputing
    Volume
    151
    Issue
    Part 1
    DOI
    https://doi.org/10.1016/j.neucom.2014.09.051
    Copyright Statement
    © 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Information and computing sciences
    Other information and computing sciences not elsewhere classified
    Engineering
    Psychology
    Publication URI
    http://hdl.handle.net/10072/125050
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    • Journal articles

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