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arXiv · 2009.01358

Change Point Detection by Cross-Entropy Maximization

Aurélien Serre·Didier Chételat·Andrea Lodi
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Abstract

Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs. We extend this framework by proposing to minimize a sum of discrepancies between segments. In particular, we propose to select the change points so as to maximize the cross-entropy between successive segments, balanced by a penalty for introducing new change points. We propose a dynamic programming algorithm to solve this problem and analyze its complexity. Experiments on two challenging datasets demonstrate the advantages of our method compared to three state-of-the-art approaches.

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Aurélien Serre, Didier Chételat, Andrea Lodi. 2020-09-02. Change Point Detection by Cross-Entropy Maximization. https://arxiv.org/abs/2009.01358

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