RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
On the error of random fourier features.arXiv preprint arXiv:1506.02785,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.