Concept drift is a serious problem confronting machine learning systems in a dynamic and ever-changing world. In order to manage concept drift it may be useful to first quantify it by measuring the distance between distributions that generate data before and after a drift. There is a paucity of methods to do so in the case of multidimensional numeric data. This paper provides an in-depth analysis of the PCA-based change detection approach, identifies shortcomings of existing methods and shows how this approach can be used to measure a drift, not merely detect it.

中文翻译：

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基于PCA的多维数据漂移和位移量化框架

在瞬息万变的世界中，概念漂移是机器学习系统面临的一个严重问题。为了管理概念漂移，通过测量在漂移之前和之后生成数据的分布之间的距离来首先对其进行量化可能是有用的。在多维数值数据的情况下，很少有这样做的方法。本文对基于PCA的变更检测方法进行了深入分析，找出了现有方法的缺点，并说明了如何将该方法用于测量漂移，而不仅仅是检测漂移。