2015
Learning Efficient Anomaly Detectors from K-NN Graphs
AISTATS 2015poster
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average K-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is dec…