Iterative diffusion-based anomaly detection

Diffusion maps, when applied to large datasets, are typically constructed by a process of sampling and out-of-sample function extension. However, the performance of anomaly detection in large data when using diffusion maps is sensitive to the chosen samples. In this paper we propose an iterative data-driven approach to improve the sample set and diffusion maps representation. By updating the sample set with suspicious points detected in the previous iteration, the constructed diffusion maps better separate the anomaly from the normal points in each iteration. Experimental results in side-scan sonar images demonstrate the improvement gained by our iterative sampling compared to random sampling and other competing detection algorithms.