Sharma, Alokanand and Paliwal, K.K. (2013) Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 5 . pp. 1-12. 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) Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Alokanand Sharma |
| Date Deposited: | 11 May 2014 23:31 |
| Last Modified: | 06 Jul 2016 22:35 |
| URI: | https://repository.usp.ac.fj/id/eprint/7336 |
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