Alternating Strategies Are Good For Low-Rank Matrix Reconstruction
Statistics Theory
2014-07-15 v1 Information Theory
math.IT
Statistics Theory
Abstract
This article focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover structured low-rank matrices, such as Hankel structure. We show that merging these two alternating strategies leads to a better performance than the existing alternating least squares (ALS) strategy. The performance is evaluated via numerical simulations.
Keywords
Cite
@article{arxiv.1407.3410,
title = {Alternating Strategies Are Good For Low-Rank Matrix Reconstruction},
author = {Kezhi Li and Martin Sundin and Cristian R. Rojas and Saikat Chatterjee and Magnus Jansson},
journal= {arXiv preprint arXiv:1407.3410},
year = {2014}
}