论文标题
使用光谱漂移检测的精确变化点检测
Precise Change Point Detection using Spectral Drift Detection
论文作者
论文摘要
概念漂移的概念是指数据生成分布随时间变化的现象。结果，机器学习模型可能会变得不准确，需要调整。在本文中，我们考虑了在无监督学习中检测这些变化点的问题。许多无监督的方法都取决于两个时间窗口的样本分布之间的差异。对于小窗口而言，此过程很吵，因此容易引起误报，并且无法在窗口中处理一个以上的漂移事件。在本文中，我们依靠漂移诱导的信号的结构特性，这些特性使用分布的内核嵌入的光谱特性。因此，我们得出了一种新的无监督的漂移检测算法，研究其数学特性，并在多个实验中证明了其有用性。
The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment. In this paper we consider the problem of detecting those change points in unsupervised learning. Many unsupervised approaches rely on the discrepancy between the sample distributions of two time windows. This procedure is noisy for small windows, hence prone to induce false positives and not able to deal with more than one drift event in a window. In this paper we rely on structural properties of drift induced signals, which use spectral properties of kernel embedding of distributions. Based thereon we derive a new unsupervised drift detection algorithm, investigate its mathematical properties, and demonstrate its usefulness in several experiments.
