Robust Covariance Anomaly Detection

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.