2021/10/08 by Euan T. McGonigle, McGonigle, Euan Thomas, Hankui Peng +1
Computer Science · Engineering · Mathematics · #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Electrical engineering #Face and Expression Recognition #Machine Learning (cs.LG) #Methodology (stat.ME) #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #Statistical and numerical algorithms #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.04044
openalex publication_date 2021/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.