Local–Global Alignment for One-Class Tabular Anomaly Detection

In this work, we address anomaly detection in tabular data under a one-class classification setting, where only normal records are available for training. We introduce local-global Representation Alignment for Tabular Anomaly Detection (Lo-GRAD), a method that detects anomalies by measuring how poorly a record's local feature representations align with its global representation. Our method assumes that anomalous records present a weaker alignment between their local and global representations. A Transformer Encoder produces both types of representations, which are optimized by a contrastive objective that increases the dot-product alignment between local-global pairs from the same record and decreases it for pairs from different records. Two auxiliary objectives strengthen this alignment: masked-feature modeling for the local representations and a reconstruction objective for the global representation. At inference, records with elevated local-global misalignment receive higher anomaly scores. We find that multiple scoring functions can effectively measure this misalignment, suggesting that the learned representations capture an anomaly signal that is not tied to a specific scoring function. Lo-GRAD also naturally provides feature-level anomaly attributions, since each local-global pair score is associated with a single feature. Across 25 ADBench datasets and without dataset-specific tuning, Lo-GRAD achieves the best macro-averaged AUROC and AUPRC compared with classical and state-of-the-art baselines, ranking first on 14 datasets for each metric.