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

Albert Akhriev, Jakub Marecek, Andrea Simonetto

AAAI 2020 pp. 3171-3178

doi:10.1609/AAAI.V34I04.5714 /aaai/2020/akhriev2020aaai-pursuit/

Abstract

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.

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Text

Akhriev et al. "Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I04.5714

Markdown

[Akhriev et al. "Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/akhriev2020aaai-pursuit/) doi:10.1609/AAAI.V34I04.5714

BibTeX

@inproceedings{akhriev2020aaai-pursuit,
  title     = {{Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise}},
  author    = {Akhriev, Albert and Marecek, Jakub and Simonetto, Andrea},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {3171-3178},
  doi       = {10.1609/AAAI.V34I04.5714},
  url       = {https://mlanthology.org/aaai/2020/akhriev2020aaai-pursuit/}
}