  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.
