Intensified regularized discriminant analysis technique

Abstract

Discriminant Analysis is utilised in working out which specific classification, a data pertains to on the basis of its needed features. Linear Discriminant Analysis(LDA) achieves the maximum class separability by projecting high-dimensional data onto a lower dimensional space. However, LDA suffers from small sample size(SSS) problem where the dimensionality of feature vector is very large compared to the number of available training samples. Regularized Discriminant Analysis(RDA) handles SSS problem of LDA with an introduction of regularization parameter(λ) and has the ability to reduce the variance. One important issue of RDA is how to automatically estimate an appropriate regularization parameter. In this paper, we propose a new algorithm to enhance the performance of RDA by effectively estimating an appropriate regularization parameter in order to reduce training time and error rate. Experiments are done using various benchmark datasets to verify the effectiveness of our proposed method with the state-of-the-art-algorithm.