Lotfi, RasoulShahsavani, DavoodArashi, Mohammad2023-09-042023-09-042022-11-01Lotfi, R.; Shahsavani, D.; Arashi, M. Classification in High Dimension Using the Ledoit–Wolf Shrinkage Method. Mathematics 2022, 10, 4069. https://doi.org/10.3390/math10214069.2227-739010.3390/math10214069http://hdl.handle.net/2263/92199Classification 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.en© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.ClassificationHigh-dimensional dataLedoit and Wolf shrinkage methodStein-type shrinkageLinear discriminant analysis (LDA)Support vector machine (SVM)Article