Robust Subspace Clustering via Smoothed Rank Approximation

@article{Kang2015RobustSC,
  title={Robust Subspace Clustering via Smoothed Rank Approximation},
  author={Zhao Kang and Chong Peng and Qiang Cheng},
  journal={IEEE Signal Processing Letters},
  year={2015},
  volume={22},
  pages={2088-2092}
}
Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this… CONTINUE READING
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Fast and accurate matrix completion via truncated nuclear norm regularization

  • J. Tang, S. Y. Yan, Z. Lin
  • IEEE Trans . Patt . Anal . Mach . Intell .
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