M-Statistic for Kernel Change-Point Detection

By Shuang Li, Yao Xie, Hanjun Dai, Le Song. 2015

124 citations recorded by the source corpus.

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been proposed for this task which make fewer assumptions on the distributions than traditional parametric approach. However, none of the existing kernel statistics has provided a computationally efficient way to characterize the extremal behavior of the statistic. Such characterization is crucial for setting the detection threshold, to control the significance level in the offline case as well as the false alarm rate (captured by the average run length) in the online case. In this paper we focus on the scenario when the amount of background data is large, and propose two related computationally efficient kernel-based statistics for change-point detection, which we call "$M$-statistics". A novel theoretical result of the paper is the characterization of the tail probability of these statistics using a new technique based on change-of-measure. Such characterization provides us accurate detection thresholds for both offline and online cases in computationally efficient manner, without the need to resort to the more expensive simulations such as bootstrapping. Moreover, our $M$-statistic can be applied to high-dimensional data by choosing a proper kernel. We show that our methods perform well in both synthetic and real world data.

Subfields: Health, Toxicology and Mutagenesis, Statistics and Probability

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Citations in the corpus

A Test of Relative Similarity For Model Selection in Generative Models (2015, methodology): “Particularly we identify that models with fewer GMMN layers and models with more nodes have more favourable samples, which is conﬁrmed by the likelihood results. samples, the Relative MMD test can provide an automatic signiﬁcance value without expensive cross-validation procedures.”