Song Feng, Kshitij Fadnis, et al.
AAAI 2020
In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformlydistributed 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.
Song Feng, Kshitij Fadnis, et al.
AAAI 2020
Girmaw Abebe Tadesse, Celia Cintas, et al.
ICML 2020
Danyang Liu, Juntao Li, et al.
AAAI 2020
Raúl Fernández Díaz, Lam Thanh Hoang, et al.
IRB-AI-DD 2025