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Given n i.i.d. samples from some unknown nominal density f0, the task of anomaly detection is to learn a mechanism that tells whether a new test point ? is nominal or anomalous, under some desired false alarm rate a. Popular non-parametric anomaly detection approaches include one-class SVM and density-based algorithms. One-class SVMis computationally efficient, but has no direct control of false alarm...
Given n nominal samples, a query point η and a significance level a, the uniformly most powerful test for anomaly detection can be to test p(η) ≤ α, where p(η) is the p-value function of η. In [1] a p-value estimator is proposed which is based on ranking some statistic over all data samples, and is shown to be asymptotically consistent. Relying on this framework we propose a new statistic for p-value...
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