PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

White Functionals for Anomaly Detection in Dynamical Systems
Marco Cuturi, Jean-Philippe Vert and Alexandre D'Aspremont
In: Advances in Neural Information Processing Systems (2009) NIPS , pp. 432-440.


We propose new methodologies to detect anomalies in discrete-time processes taking values in a probability space. These methods are based on the inference of functionals whose evaluations on successive states visited by the process are stationary and have low autocorrelations. Deviations from this behavior are used to flag anomalies. The candidate functionals are estimated in a subspace of a reproducing kernel Hilbert space associated with the original probability space considered. We provide experimental results on simulated datasets which show that these techniques compare favorably with other algorithms.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6447
Deposited By:Jean-Philippe Vert
Deposited On:08 March 2010