Abstract:
Classification using linear discriminant analysis (LDA) is challenging when the number
of variables is large relative to the number of observations. Algorithms such as LDA require the
computation of the feature vector’s precision matrices. In a high-dimension setting, due to the
singularity of the covariance matrix, it is not possible to estimate the maximum likelihood estimator
of the precision matrix. In this paper, we employ the Stein-type shrinkage estimation of Ledoit and
Wolf for high-dimensional data classification. The proposed approach’s efficiency is numerically
compared to existing methods, including LDA, cross-validation, gLasso, and SVM. We use the
misclassification error criterion for comparison.
