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

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Publication date: 10 September 2018

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.

Has companion code repository: https://github.com/jmarecek/OnlineLowRank

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