Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data

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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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Bibliographic Details
Main Authors:	Siti Zahariah, Midi, Habshah
Format:	Article
Published:	Taylor and Francis 2022
Online Access:	http://psasir.upm.edu.my/id/eprint/102186/

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