  Many modern applications require detecting change points in complex
sequential data. Most existing methods for change point detection are
unsupervised and, as a consequence, lack any information regarding what kind of
changes we want to detect or if some kinds of changes are safe to ignore. This
often results in poor change detection performance. We present a novel change
point detection framework that uses true change point instances as supervision
for learning a ground metric such that Sinkhorn divergences can be then used in
two-sample tests on sliding windows to detect change points in an online
manner. Our method can be used to learn a sparse metric which can be useful for
both feature selection and interpretation in high-dimensional change point
detection settings. Experiments on simulated as well as real world sequences
show that our proposed method can substantially improve change point detection
performance over existing unsupervised change point detection methods using
only few labeled change point instances.
