Elliptic envelope / robust Mahalanobis distance — when to use?
mediumAnswer
- Fit a robust multivariate Gaussian (Minimum Covariance Determinant, Rousseeuw-Van Driessen) → flag points with high Mahalanobis distance.
- Assumes elliptical / Gaussian normal cloud; robust to outliers via MCD.
- Fast and interpretable.
- Best for tabular data with roughly Gaussian normals (financial time series, sensor readings).
- Fails for multimodal / non-Gaussian normals — use GMM or Isolation Forest instead.
Check yourself — multiple choice
- Random
- Robust multivariate Gaussian via MCD; flag high Mahalanobis distance; assumes elliptical normal; fast, interpretable — Gaussian tabular anomaly detection
- Same as k-means
- Not real
Elliptic envelope: robust Mahalanobis + MCD; Gaussian anomaly detection.
#anomaly-detection#density
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