A High‐Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues
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2021-02-03
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Baek, Seungchul; Park, Hoyoung; Park, Junyong; A High‐Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues; Stat (2021); https://onlinelibrary.wiley.com/doi/epdf/10.1002/sta4.358
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This is the peer reviewed version of the following article: Baek, Seungchul; Park, Hoyoung; Park, Junyong; A High‐Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues; Stat (2021); https://onlinelibrary.wiley.com/doi/epdf/10.1002/sta4.358, which has been published in final form at https://doi.org/10.1002/sta4.358.
Access to this item will begin on 2022-02-03
This is the peer reviewed version of the following article: Baek, Seungchul; Park, Hoyoung; Park, Junyong; A High‐Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues; Stat (2021); https://onlinelibrary.wiley.com/doi/epdf/10.1002/sta4.358, which has been published in final form at https://doi.org/10.1002/sta4.358.
Access to this item will begin on 2022-02-03
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Abstract
High‐dimensional classification have had 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 non‐singular 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 showed that our proposed binary classification rule is better than some other rules in terms of misclassification rule through most of various synthetic data and real data sets.