@inproceedings{58dc6918e597480fa5d9e1ce76c8304f,
title = "Pursuit of low-rank models of time-varying matrices robust to sparse and measurement noise",
abstract = "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.",
author = "Albert Akhriev and Jakub Marecek and Andrea Simonetto",
note = "Publisher Copyright: {\textcopyright} 2020, Association for the Advancement of Artificial Intelligence.; 34th AAAI Conference on Artificial Intelligence, AAAI 2020 ; Conference date: 07-02-2020 Through 12-02-2020",
year = "2020",
month = jan,
day = "1",
language = "English",
series = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
publisher = "AAAI Press",
pages = "3171--3178",
booktitle = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
}