Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
This addresses anomaly detection for datasets, but appears incremental as it builds on optimal transport with a novel twist.
The paper tackled the problem of unsupervised anomaly detection by introducing Mass Repulsing Optimal Transport (MROT), which forces samples to displace mass to detect anomalies based on transportation cost, and showed that the algorithm improves over existing methods in benchmarks and fault detection problems.
Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods.