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

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Author(s)
Sharma, Alok
Paliwal, Kuldip K
Griffith University Author(s)
Year published
2015
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Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
International Journal of Machine Learning and Cybernetics
Volume
6
Issue
3
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