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arXiv · 1407.3410

Alternating Strategies Are Good For Low-Rank Matrix Reconstruction

Kezhi Li·Martin Sundin·Cristian R. Rojas·Saikat Chatterjee·Magnus Jansson
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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.

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Kezhi Li, Martin Sundin, Cristian R. Rojas, Saikat Chatterjee, Magnus Jansson. 2014-07-12. Alternating Strategies Are Good For Low-Rank Matrix Reconstruction. https://arxiv.org/abs/1407.3410

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