  Online change-point detection (OCPD) is important for application in various
areas such as finance, biology, and the Internet of Things (IoT). However, OCPD
faces major challenges due to high-dimensionality, and it is still rarely
studied in literature. In this paper, we propose a novel, online, graph-based,
change-point detection algorithm to detect change of distribution in low- to
high-dimensional data. We introduce a similarity measure, which is derived from
the graph-spanning ratio, to test statistically if a change occurs. Through
numerical study using artificial online datasets, our data-driven approach
demonstrates high detection power for high-dimensional data, while the false
alarm rate (type I error) is controlled at a nominal significant level. In
particular, our graph-spanning approach has desirable power with small and
multiple scanning window, which allows timely detection of change-point in the
online setting.
