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- W2605384178 abstract "The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices. Motivated from the classical least-mean square (LMS) algorithms for adaptive filtering, three LMS-like algorithms are proposed for estimating and tracking low-rank matrices. Performance of the proposed algorithms is provided in form of nonasymptotic bounds on the tracking mean-square error. Tracking performance of the algorithms is also studied via detailed simulations over real-world datasets." @default.
- W2605384178 created "2017-04-28" @default.
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- W2605384178 date "2017-01-01" @default.
- W2605384178 modified "2023-09-27" @default.
- W2605384178 title "Adaptive Low Rank Matrix Completion" @default.
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- W2605384178 doi "https://doi.org/10.1109/tsp.2017.2695450" @default.
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