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Linear discriminant analysis for the small sample size problem: an overview

Sharma, Alokanand and Paliwal, K.K. (2015) Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 6 (3). pp. 443-454. ISSN 1868-8071

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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 can overcome SSS problem. We have also highlighted some important datasets and software/packages.

    Item Type: Journal Article
    Subjects: T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Alokanand Sharma
    Date Deposited: 06 Jan 2016 16:04
    Last Modified: 29 Apr 2016 11:45
    URI: http://repository.usp.ac.fj/id/eprint/8647
    UNSPECIFIED

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