Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data
The main aim of this paper is to propose a novel method (RMD-MRCD-PCA) of identification of High Leverage Points (HLPs) in high-dimensional sparse data. It is to address the weakness of the Robust Mahalanobis Distance (RMD) method which is based on the Minimum Regularized Covariance Determinant (RMD...
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 出版事項: |
Taylor and Francis
2022
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/102186/ https://www.tandfonline.com/doi/abs/10.1080/02664763.2022.2093842?journalCode=cjas20 |
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