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

2018-09-10Code Available0· sign in to hype

Albert Akhriev, Jakub Marecek, Andrea Simonetto

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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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