Why do distance-based metrics fail in high-dimensional anomaly detection?

Because distance distributions collapse and anomalies are no longer separable.

Anomaly detection often assumes anomalies lie far from normal data. In high dimensions, distance concentration makes all points nearly equidistant. This removes the separation signal between normal and anomalous points. As a result, threshold-based detection fails, requiring subspace methods or density modeling in latent spaces.