2020
AAAI
AAAI 2020
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
Abstract
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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Interdisciplinary Bridge
- Data Science & Analytics and Mathematics & Optimization
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Keyword Pioneer
- matrix tracking
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Hot Topic Early Bird
- change detection
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio