Change Point Detection by Cross-Entropy Maximization
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2020
- DOI:
- 10.48550/arXiv.2009.01358
- arXiv:
- arXiv:2009.01358
- Bibcode:
- 2020arXiv200901358S
- Keywords:
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- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Signal Processing;
- Statistics - Machine Learning
- E-Print:
- Preprint