Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances tha...

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Veröffentlicht in:	IEEE transactions on information forensics and security 2018-06, Vol.13 (6), p.1446-1459
Hauptverfasser:	Hou, Elizabeth, Sricharan, Kumar, Hero, Alfred O.
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomaly detection Classification algorithms Entropy Laplace equations Linear programming maximum entropy maximum margin classifier Probabilistic logic semi-supervised classification Support vector machines
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