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

Authors

  • Albert Akhriev IBM Research
  • Jakub Marecek IBM Research
  • Andrea Simonetto IBM Research

DOI:

https://doi.org/10.1609/aaai.v34i04.5714

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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Published

2020-04-03

How to Cite

Akhriev, A., Marecek, J., & Simonetto, A. (2020). Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3171-3178. https://doi.org/10.1609/aaai.v34i04.5714

Issue

Section

AAAI Technical Track: Machine Learning