Harmonic Mean Iteratively Reweighted Least Squares for low-rank matrix recovery

@article{Kmmerle2017HarmonicMI,
  title={Harmonic Mean Iteratively Reweighted Least Squares for low-rank matrix recovery},
  author={Christian K{\"u}mmerle and Juliane Sigl},
  journal={2017 International Conference on Sampling Theory and Applications (SampTA)},
  year={2017},
  pages={489-493}
}
We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of recovering a matrix X ∈ ℝd1 × d2 of rank r ≪ min(d1, d2) from incomplete linear observations, solving a sequence of quadratic problems. The easily implementable algorithm, which we call Harmonic Mean Iteratively Reweighted Least Squares (HM-IRLS), is superior compared to state-of-the-art algorithms for the low-rank recovery problem in several performance aspects. More specifically, the strategy HM-IRLS… CONTINUE READING
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