Year: 2020
Author: Ji Li, Jian-Feng Cai, Hongkai Zhao
Journal of Computational Mathematics, Vol. 38 (2020), Iss. 2 : pp. 337–354
Abstract
We investigate the problem of robust matrix completion with a fraction of observation corrupted by sparsity outlier noise. We propose an algorithmic framework based on the ADMM algorithm for a non-convex optimization, whose objective function consists of an $\ell_1$ norm data fidelity and a rank constraint. To reduce the computational cost per iteration, two inexact schemes are developed to replace the most time-consuming step in the generic ADMM algorithm. The resulting algorithms remarkably outperform the existing solvers for robust matrix completion with outlier noise. When the noise is severe and the underlying matrix is ill-conditioned, the proposed algorithms are faster and give more accurate solutions than state-of-the-art robust matrix completion approaches.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1809-m2018-0106
Journal of Computational Mathematics, Vol. 38 (2020), Iss. 2 : pp. 337–354
Published online: 2020-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Matrix completion ADMM Outlier noise Inexact projection.
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