Iterative reweighted algorithms for matrix rank minimization

@article{Mohan2012IterativeRA,
  title={Iterative reweighted algorithms for matrix rank minimization},
  author={Karthik Mohan and Maryam Fazel},
  journal={Journal of Machine Learning Research},
  year={2012},
  volume={13},
  pages={3441-3473}
}
The problem of minimizing the rank of a matrix subject to affine constraints has applications in several areas including machine learning, and is known to be NP-hard. A tractable relaxation for this problem is nuclear norm (or trace norm) minimization, which is guaranteed to find the minimum rank matrix under suitable assumptions. In this paper, we propose a family of Iterative Reweighted Least Squares algorithms IRLS-p (with 0 ≤ p ≤ 1), as a computationally efficient way to improve over the… CONTINUE READING
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A simplified approach to recovery conditions for low rank matrices

  • B. D. Rao, K. Kreutz-Delgado.
  • 2011

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