NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection

We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, in...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 68; pp. 3515 - 3528
Main Authors: Keriven, Nicolas, Garreau, Damien, Poli, Iacopo
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algorithms
Brain modeling
Change detection
Change detection algorithms
Computational modeling
Hilbert space
Kernel
Machine Learning
method of moments
Microsoft Windows
optical computing
Radio frequency
Signal processing algorithms
Statistical methods
Statistics
Streaming media
Radio frequency
Microsoft Windows
Computational modeling
Signal processing algorithms
Streaming media
Brain modeling
Kernel
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features (RFs) to efficiently use the Maximum Mean Discrepancy as a distance between distributions, furthermore exploiting recent optical hardware to compute high-dimensional RFs in near constant time. We show that our method is significantly faster than usual non-parametric methods for a given accuracy.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.2990597