Adaptive Low-Rank Matrix Completion

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.

DOI: 10.1109/TSP.2017.2695450

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Cite this paper

@article{Tripathi2017AdaptiveLM, title={Adaptive Low-Rank Matrix Completion}, author={Ruchi Tripathi and Boda Mohan and Ketan Rajawat}, journal={IEEE Transactions on Signal Processing}, year={2017}, volume={65}, pages={3603-3616} }