Supervised Kernel Change Point Detection with Partial Annotations

Abstract

In this article, we propose an automatic procedure to calibrate change point detection algorithms. Our approach expands on the ability of an expert to provide very rough segmentation estimates, called partial annotations, for a few signal examples. Our contribution consists in a supervised strategy to learn a kernel Mahalanobis metric, which, once combined with a detection algorithm, can replicate the expert's segmentation strategy on new signals. Contrary to previous works, our approach is non-parametric, supervised and naturally accommodates partial annotations. Experiments on real-world data show that supervision significantly improves detection performance.