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

  title={Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise},
  author={Albert Akhriev and Jakub Marecek and Andrea Simonetto},
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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  • Mathematics, Computer Science
  • SIAM J. Matrix Anal. Appl.
  • 2017
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