Change Point Detection by Cross-Entropy Maximization

TL;DR

This paper introduces a novel change point detection method that maximizes cross-entropy between segments, offering an alternative to traditional cost-minimization approaches, and demonstrates its effectiveness through experiments.

Contribution

It proposes a new framework for change point detection based on cross-entropy maximization and provides a dynamic programming solution with empirical validation.

Findings

Outperforms three state-of-the-art methods on challenging datasets

Effective in detecting change points by maximizing segment discrepancies

Offers a computationally analyzed algorithm for practical use

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.