Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
This addresses the challenge of robust low-rank modeling for dynamic systems, which is incremental as it builds on existing methods with specific noise robustness.
The paper tackles the problem of tracking time-varying low-rank models of matrices in the presence of both measurement and sparse noise, achieving a bounded tracking error in theory and scalable, encouraging results on a benchmark dataset.
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.