Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case
Author(s)
Paliwal, Kuldip
Sharma, Alok
Griffith University Author(s)
Year published
2011
Metadata
Show full item recordAbstract
The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.
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Journal Title
Journal of Pattern Recognition Research
Volume
X
Publisher URI
Subject
Artificial intelligence not elsewhere classified