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：检测数据流中的突然分布变化（即变化点检测）是统计学和信号处理中的基本问题。我们提出了一种通过训练神经网络（NN）进行在线变化点检测的新方法，该方法利用CUSUM递归在测试样本上评估训练得到的判别函数，并逐步累积检测统计量。该思路基于以下观察：通过逻辑损失训练神经网络可能产生对数似然函数。我们通过合成数据与真实数据验证了NN-CUSUM在高维数据检测中的良好性能。