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News/Interpretable Anomaly and Drift Detection with Gaussian Mixture Models
arxiv
PublishedJuly 27, 2026 at 4:00 AM
—neutral

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.16811v2 Announce Type: replace 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 on seven public

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Discussion
Mentioned models
07
  • 01
    Gaussian Mixture Models (GMMs)
  • 02
    Isolation Forest
  • 03
    Local Outlier Factor
  • 04
    one-class SVM
  • 05
    ECOD
  • 06
    COPOD
  • 07
    autoencoder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#anomaly detection#machine learning#interpretable models#distributional drift

No replies yet. Be first.

Mentioned models
07
  • 01
    Gaussian Mixture Models (GMMs)
  • 02
    Isolation Forest
  • 03
    Local Outlier Factor
  • 04
    one-class SVM
  • 05
    ECOD
  • 06
    COPOD
  • 07
    autoencoder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#anomaly detection#machine learning#interpretable models#distributional drift

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Originally published on arxiv ↗
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