Robust Filtering and Correlation-Aligned Diffusion for Unsupervised Tabular Anomaly Detection

Unsupervised anomaly detection trains on unlabeled data that contain anomalies. We address this contamination with two data-centric steps. First, a FastMCD-based pre-training filter removes rows outside the robust covariance support; it improves most detectors we compare. Second, we replace only the isotropic forward corruption of Diffusion Time Estimation (DTE), a diffusion-based detector, with correlation-aligned noise. Estimating this correlation from the retained rows, anomalies included, beats every cleaner set of rows we tested. Anisotropy-aware DTE (ADTE) combines these two steps and, in its categorical variant ADTE-C, a regression head that stabilizes results across seeds. On the 57 ADBench groups under the unsupervised protocol, ADTE-C reaches 77.0 AUCROC, to our knowledge the highest reported. Removing any one of the three components significantly lowers ADTE-C's mean AUCROC.