Robust Subspace Clustering via Smoothed Rank Approximation

  title={Robust Subspace Clustering via Smoothed Rank Approximation},
  author={Zhao Kang and Chong Peng and Qiang Cheng},
  journal={IEEE Signal Processing Letters},
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
Recent Discussions
This paper has been referenced on Twitter 6 times over the past 90 days. VIEW TWEETS
17 Citations
27 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 17 extracted citations


Publications referenced by this paper.
Showing 1-10 of 27 references

Fast and accurate matrix completion via truncated nuclear norm regularization

  • J. Tang, S. Y. Yan, Z. Lin
  • IEEE Trans . Patt . Anal . Mach . Intell .
  • 2013

Similar Papers

Loading similar papers…