发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种自适应在线核变点检测方法，通过梯度更新遗忘因子并利用最大均值差异统计量，实现高效检测流数据分布变化，性能优于现有核方法。
AI 中文摘要
我们提出了一种适用于流数据的自适应在线基于核的变点检测方法，能够检测底层数据分布中的广泛变化。该方法维护观测值的递归加权再生核希尔伯特空间表示，并通过由当前观测值与过去的加权经验分布之间的最大均值差异型统计量驱动的基于梯度的过程，自适应地更新遗忘因子。这种自调节机制使检测器能够自适应其有效记忆和对底层过程变化的响应性。模拟结果表明，所提出的方法在广泛的分布变化中实现了强检测性能。此外，我们提出的方法通过递归更新保持恒定的计算和存储成本，并且与竞争方法相比在计算上非常高效。在模拟数据和基准真实世界数据集上的实验显示，相对于其他几种领先的基于核的方法，性能有所提升。
英文摘要
We propose an adaptive online kernel-based changepoint detection method for streaming data that is capable of detecting a broad range of changes in the underlying data distribution. The method maintains a recursively-weighted reproducing kernel Hilbert space representation of observations and adaptively updates the forgetting factor through a gradient-based procedure driven by a maximum mean discrepancy-type statistic between the current observation and the weighted empirical distribution of the past. This self-tuning mechanism allows the detector to adapt its effective memory and responsiveness to changes in the underlying process. Simulation results demonstrate that the proposed method achieves strong detection performance across a wide range of distributional changes. Further, our proposed approach maintains constant computational and storage cost through recursive updates, and is very computationally efficient in comparison to competing methods. Experiments on both simulated data and benchmark real-world datasets show improved performance over several other leading kernel-based methods.
Comments50 pages, 25 figures, 6 tables
