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Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensional data.
Learning Efficient Anomaly Detectors from $K$-NN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensional data.
A Rank-SVM Approach to Anomaly Detection
The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.
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