When the number of variables is considerable in comparison to the number of observations, classification using linear discriminant analysis (LDA) is difficult. The computation of the feature vector\\\\\\\\\\\\\\\'s precision matrices is necessary for algorithms like LDA. The covariance matrix\\\\\\\\\\\\\\\'s singularity prevents the estimation of the maximum likelihood estimator of the precision matrix in a high-dimension environment. This talk implements shrinkage estimation to high-dimensional data classification. The effectiveness of the suggested method is quantitatively contrasted with other approaches, such as LDA, cross-validation, gLasso, and SVM.

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