5714@AAAI

#1 Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise [PDF] [Copy] [Kimi]

Authors: Albert Akhriev ; Jakub Marecek ; 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.