# Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

@article{Lu2005RegularizationSO, title={Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition}, author={Juwei Lu and Konstantinos N. Plataniotis and Anastasios N. Venetsanopoulos}, journal={Pattern Recognit. Lett.}, year={2005}, volume={26}, pages={181-191} }

It is well-known that the applicability of linear discriminant analysis (LDA) to high-dimensional pattern classification tasks such as face recognition often suffers from the so-called ''small sample size'' (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper, we propose a new LDA method that attempts to address the SSS problem using a regularized Fisher's separability criterion. In addition, a scheme of… Expand

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