  The objective of change-point detection is to discover abrupt property
changes lying behind time-series data. In this paper, we present a novel
statistical change-point detection algorithm based on non-parametric divergence
estimation between time-series samples from two retrospective segments. Our
method uses the relative Pearson divergence as a divergence measure, and it is
accurately and efficiently estimated by a method of direct density-ratio
estimation. Through experiments on artificial and real-world datasets including
human-activity sensing, speech, and Twitter messages, we demonstrate the
usefulness of the proposed method.
