Matrix completion of noisy graph signals via proximal gradient minimization
Document typeConference lecture
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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.
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CitationGimenez, 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.