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  • Linear discriminant analysis for the small sample size problem: An overview

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    SharmaPUB407.pdf (590.7Kb)
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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
    Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which ...
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    Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/packages.
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    Journal Title
    International Journal of Machine Learning and Cybernetics
    Volume
    6
    Issue
    3
    DOI
    https://doi.org/10.1007/s13042-013-0226-9
    Copyright Statement
    © 2015 Springer Berlin Heidelberg. This is an electronic version of an article published in International Journal of Machine Learning and Cybernetics, Vol 6 Issue 3, pages 443-454, 2015. International Journal of Machine Learning and Cybernetics is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/101329
    Collection
    • Journal articles

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