Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

By PulseAugur Editorial · [1 sources] · 2026-07-21 04:00

Researchers have developed a method using Gaussian Mixture Models (GMMs) for interpretable anomaly and drift detection in data streams. This approach automatically selects the number of mixture components using the Bayesian Information Criterion and scores individual observations based on their negative log-likelihood. The model extends to distributional drift detection by identifying AI

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Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

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arXiv cs.LG TIER_1 English(EN) · Behnam Asadi · 2026-07-21 04:00

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607.16811v1 Announce Type: new Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams. We make three practical choices explicit and evaluate them …