# Online Robust Principal Component Analysis with Change Point Detection

arXiv: https://arxiv.org/abs/1702.05698 · HTML: https://mlsanity.com/paper/1702.05698/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/2109.09889/) (2021)
- [Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization](https://mlsanity.com/paper/2409.07275/) (2024)
- [Real-time Fuel Leakage Detection via Online Change Point Detection](https://mlsanity.com/paper/2410.09741/) (2024)
- [Heavy-Tailed Principal Component Analysis](https://mlsanity.com/paper/2603.11308/) (2026)
- [AIGS: Adaptive Incremental Gating System for Online Representation Learning in Non-Stationary Data Streams](https://mlsanity.com/paper/2610.02661/) (2026)

## Abstract (arXiv)

Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only slowly changing subspace but also abruptly changed subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can detect change points of the underlying subspaces. Extensive simulation studies demonstrate the superior performance of OMWRPCA compared with other state-of-art approaches. We also apply the algorithm for real-time background subtraction of surveillance video.