Change-Point Detection of Gaussian Graph Signals with Partial Information

Abstract

In a change-point detection problem, a sequence of signals switches from one distribution to another at an unknown time step, and the goal is to quickly and reliably detect this change. By providing new insight into signal processing and data analysis, graph signal processing promises various applications including image processing and sensor network analysis, and becomes an emerging field of research. In this work, we formulate the problem of change-point detection on graph. Under the reasonable assumption of normality, we propose a CUSUM-based algorithm for change-point detection with an arbitrary, unknown and perhaps time-varying mean shift after the change-point. We further propose a decentralized, distributed algorithm, which requires no fusion center, to reduce computational complexity, as well as costs and delays of communication. Numerical results on both synthetic and real-world data demonstrate that our algorithms are efficient and accurate.

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