Offline detection of change-points in the mean for stationary graph signals

TL;DR

This paper introduces an offline method for detecting change-points in the mean of stationary graph signals by leveraging spectral domain analysis and sparsity, with proven theoretical guarantees and demonstrated effectiveness.

Contribution

It presents a novel spectral domain change-point detection method for graph signals that exploits sparsity and includes an automatic model selection procedure.

Findings

Effective detection of change-points demonstrated in experiments.

Method provides non-asymptotic oracle inequality guarantees.

Spectral sparsity is effectively utilized for improved detection.

Abstract

This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of a multivariate signal defined over the nodes of a known graph. We propose an offline method that relies on the concept of graph signal stationarity and allows the convenient translation of the problem from the original vertex domain to the spectral domain (Graph Fourier Transform), where it is much easier to solve. Although the obtained spectral representation is sparse in real applications, to the best of our knowledge this property has not been sufficiently exploited in the existing related literature. Our change-point detection method adopts a model selection approach that takes into account the sparsity of the spectral representation and determines automatically the number of change-points. Our detector comes with a proof of a non-asymptotic oracle inequality. Numerical experiments demonstrate the performance of the proposed method.