Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
This paper introduces an Optimal Transport (OT)-based framework for anomaly detection, designed to detect deviations from normal behaviour in time-series sensor data, that requires minimal user input and adapts to real-time data without the need for labelled training data.
Abigail Langbridge, Fearghal O'Donncha, James T. Rayfield et al.
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