论文标题
最佳子采样以增强内核顺序更改点检测的能力
Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection
论文作者
论文摘要
我们提出了一种新的方案，以增强基于内核最大平均差异的检测能力。我们提出的方案在检测过程之前具有最佳的历史数据子采样，以解决从大量历史数据中随机子样本产生的功率损失。我们将我们提出的方案应用于扫描$ b $和内核累积总和（CUSUM）程序，并从广泛的数值实验中观察到了改善的性能。
We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data. We apply our proposed scheme to both Scan $B$ and Kernel Cumulative Sum (CUSUM) procedures, and improved performance is observed from extensive numerical experiments.
