nips nips2009 nips2009-257 nips2009-257-reference knowledge-graph by maker-knowledge-mining
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Author: Marco Cuturi, Jean-philippe Vert, Alexandre D'aspremont
Abstract: 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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