# Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise

arXiv: https://arxiv.org/abs/1809.03550 · HTML: https://mlsanity.com/paper/1809.03550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

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

- [Streaming data preprocessing via online tensor recovery for large environmental sensor networks](https://mlsanity.com/paper/2109.00596/) (2021)

## Abstract (arXiv)

In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed ``sparse'' noise. In theory, we bound the tracking error. In practice, our use of randomised coordinate descent is scalable and allows for encouraging results on changedetection net, a benchmark.