Partition-based outlier detection for high dimensional data

Abstract

Abstract Outlier detection has become increasingly important for high dimensional data (HDD) before data analysis. Although the multivariate outlier detection methods (ODMs) have been extensively studied in the research literature, there are few ODMs for ODMs when a correlation exists between data’s components. In this paper, we introduce a partition-based estimator of the minimum covariance determinant for HDD, along with a rapid algorithm designed to identify the clean subset. Additionally, an OD threshold is employed by examining the asymptotic distribution of the outlying measure distance to pinpoint outliers. Moreover, a one-step reweighting scheme is introduced to enhance the detection efficiency of the process. At last, simulation results demonstrate that the proposed RPMCD methods exhibit superior finite sample performance compared to other existing techniques.