  We propose a new multiple change-point detection framework for multivariate
and non-Euclidean data. First, we combine graph-based statistics with wild
binary segmentation or seeded binary segmentation to search for a pool of
candidate change-points. We then prune the candidate change-points through a
novel goodness-of-fit statistic. Numerical studies show that this new framework
outperforms existing methods under a wide range of settings. The resulting
change-points can further be arranged hierarchically based on the
goodness-of-fit statistic. The new framework is illustrated on a Neuropixels
recording of an awake mouse.
