# High-dimensional changepoint estimation via sparse projection

arXiv: https://arxiv.org/abs/1606.06246 · HTML: https://mlsanity.com/paper/1606.06246/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2016.

## Built on by (most cited first, 20 of 20)

- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Testing Stationarity and Change Point Detection in Reinforcement Learning](https://mlsanity.com/paper/2203.01707/) (2022)
- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees](https://mlsanity.com/paper/2105.10017/) (2021)
- [High dimensional change-point detection: a complete graph approach](https://mlsanity.com/paper/2203.08709/) (2022)
- [Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA](https://mlsanity.com/paper/2202.04719/) (2022)
- [Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series](https://mlsanity.com/paper/2304.07003/) (2023)
- [Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings](https://mlsanity.com/paper/2601.18788/) (2026)
- [Identification of brain states, transitions, and communities using functional MRI](https://mlsanity.com/paper/2101.10617/) (2021)
- [Inference for Change Points in High Dimensional Mean Shift Models](https://mlsanity.com/paper/2107.09150/) (2021)
- [Inferring Change Points in High-Dimensional Regression via Approximate Message Passing](https://mlsanity.com/paper/2404.07864/) (2024)
- [WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection](https://mlsanity.com/paper/2506.08066/) (2025)
- [Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation](https://mlsanity.com/paper/2510.03437/) (2025)
- [TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring](https://mlsanity.com/paper/2512.01562/) (2025)
- [High-Dimensional Change Point Detection via Graph Spanning Ratio](https://mlsanity.com/paper/2512.07541/) (2025)
- [Change-Point Detection via Piecewise Linear Fitting Using MIP](https://mlsanity.com/paper/2602.11947/) (2026)
- [The Sample Complexity of Multiple Change Point Identification under Bandit Feedback](https://mlsanity.com/paper/2605.13252/) (2026)
- [High-dimensional nonparametric changepoint detection via low-rank degree-two density projection](https://mlsanity.com/paper/2608.13922/) (2026)
- [High-Dimensional Nonparametric Change-Point Detection via Low-Rank Degree-Three Density Projection](https://mlsanity.com/paper/2608.15466/) (2026)

## Abstract (arXiv)

Changepoints are a very common feature of Big Data that arrive in the form of a data stream. In this paper, we study high-dimensional time series in which, at certain time points, the mean structure changes in a sparse subset of the coordinates. The challenge is to borrow strength across the coordinates in order to detect smaller changes than could be observed in any individual component series. We propose a two-stage procedure called `inspect' for estimation of the changepoints: first, we argue that a good projection direction can be obtained as the leading left singular vector of the matrix that solves a convex optimisation problem derived from the CUSUM transformation of the time series. We then apply an existing univariate changepoint estimation algorithm to the projected series. Our theory provides strong guarantees on both the number of estimated changepoints and the rates of convergence of their locations, and our numerical studies validate its highly competitive empirical performance for a wide range of data generating mechanisms. Software implementing the methodology is available in the R package `InspectChangepoint'.