A high‐dimensional classification rule using sample covariance matrix equipped with adjusted estimated eigenvalues. Issue 1 (24th June 2021)

• Record Type: Journal Article
• Authors: Baek, Seungchul; Park, Hoyoung; Park, Junyong
• Is Part Of: Stat. Volume 10:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-24
• Subjects: adjusted estimated eigenvalue -- high‐dimensional classification -- linear discriminant analysis; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.358
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : High‐dimensional classification has challenges mainly due to the singularity issue of the sample covariance matrix. In this work, we propose a different approach to get a more reliable sample covariance matrix by adjusting the estimated eigenvalues. This procedure also brings us a nonsingular matrix as a by‐product. We improve the optimization procedure to obtain a linear classifier by incorporating the adjusted sample covariance matrix and a shrinkage mean vector into the original optimization problem. We have shown that our proposed binary classification rule is better than some other rules in terms of the misclassification rate through most of various synthetic data and real data sets.