Subspace Change-Point Detection via Low-Rank Matrix Factorisation
This addresses the need for efficient change-point detection in high-dimensional data for applications like monitoring and anomaly detection, representing an incremental improvement over existing methods.
The paper tackles the problem of detecting changes in the underlying subspace structure of high-dimensional multivariate time series, proposing a method based on low-rank matrix factorisation that effectively identifies multiple change-points, with experimental results showing advantages over state-of-the-art methods on synthetic and real datasets.
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.