ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions. (arXiv:2201.00382v1 [cs.LG])

Outlier detection refers to the identification of data points that deviate
from a general data distribution. Existing unsupervised approaches often suffer
from high computational cost, complex hyperparameter tuning, and limited
interpretability, especially when working with large, high-dimensional
datasets. To address these issues, we present a simple yet effective algorithm
called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which
is inspired by the fact that outliers are often the "rare events" that appear
in the tails of a distribution. In a nutshell, ECOD first …

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