TY - GEN
T1 - Pursuit of low-rank models of time-varying matrices robust to sparse and measurement noise
AU - Akhriev, Albert
AU - Marecek, Jakub
AU - Simonetto, Andrea
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85104194492
M3 - Conference contribution
AN - SCOPUS:85104194492
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 3171
EP - 3178
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI Press
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -