Subspace expanders and matrix rank minimization

@article{Oymak2011SubspaceEA,
  title={Subspace expanders and matrix rank minimization},
  author={S. Oymak and M. Khajehnejad and B. Hassibi},
  journal={2011 IEEE International Symposium on Information Theory Proceedings},
  year={2011},
  pages={2308-2312}
}
  • S. Oymak, M. Khajehnejad, B. Hassibi
  • Published 2011
  • Mathematics, Computer Science
  • 2011 IEEE International Symposium on Information Theory Proceedings
  • Matrix rank minimization (RM) problems recently gained extensive attention due to numerous applications in machine learning, system identification and graphical models. In RM problem, one aims to find the matrix with the lowest rank that satisfies a set of linear constraints. The existing algorithms include nuclear norm minimization (NNM) and singular value thresholding. Thus far, most of the attention has been on i.i.d. Gaussian or Bernoulli measurement operators. In this work, we introduce a… CONTINUE READING
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