2026-10-15T03:47:48Z https://www.scidb.cn/oai

10.57760/sciencedb.40650 2026-06-22T15:02:16Z

2026-06-22 Robust Outlier Detection for High-Dimensional Interval-Valued Data (Appendix) doi:10.57760/sciencedb.40650 en The dataset is provided as the appendix to the identified paper, Robust Outlier Detection for High-Dimensional Interval-Valued Data. Experiment 1.1 conducts outlier detection experiments for low-dimensional and moderately high-dimensional interval-valued data, evaluating the proposed method when the covariance matrix can be estimated directly or regularized. Experiment 1.2 conducts outlier detection experiments for high-dimensional interval-valued data, examining the applicability and robustness of the proposed method when (p \geq 2n) and the covariance matrix is singular. Experiment 1.3 provides comparative experiments under different contamination proportions and outlier types, testing the method’s ability to detect center outliers, range outliers, and mixed outliers. The evaluated methods include traditional Mahalanobis-distance-based approaches, robust covariance estimation methods, and the proposed high-dimensional interval-valued outlier detection method based on modified Mahalanobis distance and a BH-FDR adaptive threshold. High-dimensional interval-valued data ; Outlier detection; Robust covariance estimation Tian Wan Li Zhongyi Peng Yijie PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank