RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection

Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are often efficient, but they usually rely on a fixed data view and a single notion of abnormality. Deep anomaly detectors can learn more flexible scoring functions, but they are substantially slower and difficult to tune in unsupervised settings due to the lack of a reliable supervisory signal. We propose RGLD, a randomized global-local density estimator for efficient unsupervised tabular anomaly detection. RGLD combines a global random-feature density branch, which identifies samples in broadly low-density regions, with a local neighbor branch, which detects samples that are weakly supported by nearby observations. Both branches operate over feature-bagged randomized views, allowing RGLD to expose anomaly evidence that may be hidden in any single representation. We evaluate RGLD on 47 tabular datasets against 23 statistical and deep anomaly detection baselines in the fully unsupervised setting. RGLD achieves competitive detection accuracy, with 77.50 AUROC and 38.59 AUPRC, while obtaining the most AUROC dataset wins. It runs 4.9x faster than ADERH and 50x–580x faster than the evaluated deep detectors, yielding a strong accuracy–efficiency tradeoff.