Detecting an abrupt distributional shift of the data stream, known as change-point detection, is a fundamental problem in statistics and signal processing. We present a new approach for online change-point detection by training neural networks (NN), and sequentially cumulating the detection statistics by evaluating the trained discriminating function on test samples by a CUSUM recursion. The idea is based on the observation that training neural networks through logistic loss may lead to the log-likelihood function. We demonstrated the good performance of NN-CUSUM in the detection of high-dimensional data using both synthetic and real-world data.

翻译：检测数据流的突变分布变化（即变点检测）是统计学与信号处理中的基本问题。本文提出一种在线变点检测新方法：通过训练神经网络（NN）累积检测统计量，并利用CUSUM递归对测试样本评估训练所得的判别函数。该思路基于以下观察：通过逻辑损失训练神经网络可逼近对数似然函数。我们通过合成数据与实际数据验证了NN-CUSUM方法在高维数据检测中的优异性能。