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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Taylor and Francis
2022
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