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

In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformlydistributed 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.

Authors

• Albert Akhriev
• Jakub Marecek Czech Technical University
• Andrea Simonetto

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