Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation

TitleFast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Publication TypeJournal Article
Year of Publication2014
AuthorsAleksandr Y. Aravkin, Rajiv Kumar, Hassan Mansour, Ben Recht, Felix J. Herrmann
JournalSIAM Journal on Scientific Computing
Volume36
Number5
PageS237-S266
Month10
Keywordsfactorized formulations, matrix completion, robust statistics, seismic trace interpolation, variational optimization
Abstract

Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic data interpolation. In this paper, we consider matrix completion formulations designed to hit a target data-fitting error level provided by the user, and we propose an algorithm called LR-BPDN that is able to exploit factorized formulations to solve the corresponding optimization problem. Since practitioners typically have strong prior knowledge about target error level, this innovation makes it easy to apply the algorithm in practice, leaving only the factor rank to be determined. Within the established framework, we propose two extensions that are highly relevant to solving practical challenges of data interpolation. First, we propose a weighted extension that allows known subspace information to improve the results of matrix completion formulations. We show how this weighting can be used in the context of frequency continuation, an essential aspect to seismic data interpolation. Second, we propose matrix completion formulations that are robust to large measurement errors in the available data. We illustrate the advantages of LR-BPDN on collaborative filtering problems using the MovieLens 1M and 10M and Netflix 100M datasets. Then we use the new method, along with its robust and subspace reweighted extensions, to obtain high-quality reconstructions for large-scale seismic interpolation problems with real data, even in the presence of data contamination.

Notes

(SISC)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Journals/SIAMJournalOnScientificComputing/2014/aravkin2014SISCfmd/aravkin2014SISCfmd.pdf
DOI10.1137/130919210
URL1

http://epubs.siam.org/doi/abs/10.1137/130919210

Citation Keyaravkin2014SISCfmd