Rank Minimization or Nuclear-Norm Minimization: Are We Solving the Right Problem?

@article{Dai2014RankMO,
  title={Rank Minimization or Nuclear-Norm Minimization: Are We Solving the Right Problem?},
  author={Yuchao Dai and Hongdong Li},
  journal={2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)},
  year={2014},
  pages={1-8}
}
  • Yuchao Dai, Hongdong Li
  • Published 2014
  • Mathematics, Computer Science
  • 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Low rank method or rank-minimization has received considerable attention from recent computer vision community. Due to the inherent computational complexity of rank problems, the non-convex rank function is often relaxed to its convex relaxation, i.e. the nuclear norm. Thanks to recent progress made in the filed of compressive sensing (CS), vision researchers who are practicing CS are fully aware, and conscious, of the convex relaxation gap, as well as under which condition (e.g. Restricted… Expand
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