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Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
September 10, 2018 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Albert Akhriev, Jakub Marecek, Andrea Simonetto
arXiv ID
1809.03550
Category
math.OC: Optimization & Control
Cross-listed
cs.CV,
math.ST
Citations
8
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
Abstract
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.
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