Anomaly Detection with Score Distribution Discrimination
Minqi Jiang, Songqiao Han, Hailiang Huang
Abstract
Recent studies give more attention to the anomaly detection (AD) methods that can leverage a handful of labeled anomalies along with abundant unlabeled data. These existing anomaly-informed AD methods rely on manually predefined score target(s), e.g., prior constant or margin hyperparameter(s), to realize discrimination in anomaly scores between normal and abnormal data. However, such methods would be vulnerable to the existence of anomaly contamination in the unlabeled data, and also lack adaptation to different data scenarios.
Topics & Concepts
Anomaly detectionLeverage (statistics)Anomaly (physics)Margin (machine learning)Computer scienceHyperparameterArtificial intelligencePattern recognition (psychology)Data miningMachine learningPhysicsCondensed matter physicsAnomaly Detection Techniques and ApplicationsData-Driven Disease SurveillanceNetwork Security and Intrusion Detection