基于低秩矩阵分解的子空间变点检测
Subspace Change-Point Detection via Low-Rank Matrix Factorisation
摘要：
多变量时间序列通常可以具有大量的维度，无论是由于收集的大量特征还是由于如何处理数据源。通常，高维时间序列的主要结构可以很好地由低维子空间表示。由于大量的数据是在很长一段时间内收集的，因此可以合理地假设基本的子空间结构会随着时间的推移而变化。在这项工作中，我们提出了一种基于低秩矩阵分解的变点检测方法，可以检测多变量时间序列的底层子空间中的多个变化。在合成数据集和真实数据集上的实验结果证明了该方法的有效性及其与各种最先进方法的比较优势。
英文摘要：
Multivariate time series can often have a large number of dimensions, whether it is due to the vast amount of collected features or due to how the data sources are processed. Frequently, the main structure of the high-dimensional time series can be well represented by a lower dimensional subspace. As vast quantities of data are being collected over long periods of time, it is reasonable to assume that the underlying subspace structure would change over time. In this work, we propose a change-point detection method based on low-rank matrix factorisation that can detect multiple changes in the underlying subspace of a multivariate time series. Experimental results on both synthetic and real data sets demonstrate the effectiveness of our approach and its advantages against various state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
