Kim-Hung Le

Tuning-Free One-Class Discriminant Learning for Tabular Anomaly Detection

Anomaly detection (AD) on real-world tabular data is challenged by scarce labels, diverse anomaly types, and high sensitivity to data-specific hyperparameter tuning. We propose Discriminant Vector Machine for Anomaly detection (DVM-AD), a robust one-class method derived from discriminant analysis wi…