# Subspace Change-Point Detection via Low-Rank Matrix Factorisation

arXiv: https://arxiv.org/abs/2110.04044 · HTML: https://mlsanity.com/paper/2110.04044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit](https://mlsanity.com/paper/1507.01238/) (2015)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [Multiple-change-point detection for high dimensional time series via sparsified binary segmentation](https://mlsanity.com/paper/1611.08639/) (2016)
- [Simultaneous multiple change-point and factor analysis for high-dimensional time series](https://mlsanity.com/paper/1612.06928/) (2016)
- [Provable Dynamic Robust PCA or Robust Subspace Tracking](https://mlsanity.com/paper/1705.08948/) (2017)
- [Fast Nonconvex Deconvolution of Calcium Imaging Data](https://mlsanity.com/paper/1802.07380/) (2018)

## Built on by (most cited first, 1 of 1)

- [Random features for Grassmannian kernel approximation with bounded rank-one projections](https://mlsanity.com/paper/2608.04227/) (2026)

## Abstract (arXiv)

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.