Marco Cuturi, Princeton University

White Functionals for Anomaly Detection in Dynamical Systems
Nov 24, 2009, 12:00 pm1:00 pm
213 - Sherrerd Hall


Event Description

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.

For the full paper, see "White functionals for anomaly detection in dynamical systems".