White Functionals for Anomaly Detection in Dynamical Systems
Abstract
We propose new methodologies to detect anomalies in discrete-time processes taking values in a set. The method is based on the inference of functionals whose evaluations on successive states visited by the process have low autocorrelations. Deviations from this behavior are used to flag anomalies. The candidate functionals are estimated in a subset of a reproducing kernel Hilbert space associated with the set where the process takes values. We provide experimental results which show that these techniques compare favorably with other algorithms.
Cite
Text
Cuturi et al. "White Functionals for Anomaly Detection in Dynamical Systems." Neural Information Processing Systems, 2009.Markdown
[Cuturi et al. "White Functionals for Anomaly Detection in Dynamical Systems." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/cuturi2009neurips-white/)BibTeX
@inproceedings{cuturi2009neurips-white,
title = {{White Functionals for Anomaly Detection in Dynamical Systems}},
author = {Cuturi, Marco and Vert, Jean-philippe and D'aspremont, Alexandre},
booktitle = {Neural Information Processing Systems},
year = {2009},
pages = {432-440},
url = {https://mlanthology.org/neurips/2009/cuturi2009neurips-white/}
}