Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
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.
How to Cite
Akhriev, A., Marecek, J., & Simonetto, A. (2020). Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3171-3178. https://doi.org/10.1609/aaai.v34i04.5714
AAAI Technical Track: Machine Learning