Robust Covariance Anomaly Detection

TR: Robust Kovaryans Tabanlı Anomali Tespiti

In One Line

A statistical approach that performs anomaly detection through covariance estimation that is more robust to outliers.

Robust covariance methods provide more reliable estimates of center and spread when classical covariance estimation is distorted by outliers. Anomaly detection can then be performed using Mahalanobis-like distance measures. These methods can be useful especially in multivariate and medium-scale datasets. However, distributional form and feature scaling still remain important considerations.