Nonconvex alternating direction method of multipliers for distributed sparse principal component analysis

@article{Hajinezhad2015NonconvexAD,
  title={Nonconvex alternating direction method of multipliers for distributed sparse principal component analysis},
  author={Davood Hajinezhad and Mingyi Hong},
  journal={2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
  year={2015},
  pages={255-259}
}
In this paper, we propose distributed algorithms to perform sparse principal component analysis (SPCA). The key benefit of the proposed algorithms is their ability to handle distributed data sets. Our algorithms are able to handle a few sparse-promoting regularizers (i.e., the convex norm and the nonconvex log-sum penalty) as well as different forms of data partition (i.e., partition across rows or columns of the data matrix). Our methods are based on a nonconvex ADMM framework, and they are… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 12 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 33 references

Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems

  • ——
  • the Proceedings of the IEEE ICASSP, 2015.
  • 2015

Similar Papers

Loading similar papers…