Matrix completion of noisy graph signals via proximal gradient minimization
Visualitza/Obre
10.1109/ICASSP.2017.7952996
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/110536
Tipus de documentComunicació de congrés
Data publicació2017
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
This paper takes on the problem of recovering the missing entries of an incomplete matrix, which is known as matrix completion, when the columns of the matrix are signals that lie on a graph and the available observations are noisy. We solve a version of the problem regularized with the Laplacian quadratic form by means of the proximal gradient method, and derive theoretical bounds on the recovery error. Moreover, in order to speed up the convergence of the proximal gradient, we propose an initialization method that utilizes the structural information contained in the Laplacian matrix of the graph.
Descripció
©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitacióGimenez, P., Pages, A. Matrix completion of noisy graph signals via proximal gradient minimization. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings". New Orleans: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 4441-4445.
ISBN978-1-5090-4117-6
Versió de l'editorhttp://ieeexplore.ieee.org/document/7952996/
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