期刊文献+

Stable recovery of low-rank matrix via nonconvex Schatten p-minimization 被引量:3

Stable recovery of low-rank matrix via nonconvex Schatten p-minimization
原文传递
导出
摘要 In this paper, a sufficient condition is obtained to ensure the stable recovery(ε≠ 0) or exact recovery(ε = 0) of all r-rank matrices X ∈ Rm×nfrom b = A(X) + z via nonconvex Schatten p-minimization for anyδ4r∈ [3~(1/2))2, 1). Moreover, we determine the range of parameter p with any given δ4r∈ [(3~(1/2))/22, 1). In fact, for any given δ4r∈ [3~(1/2))2, 1), p ∈(0, 2(1- δ4r)] suffices for the stable recovery or exact recovery of all r-rank matrices. In this paper, a sufficient condition is obtained to ensure the stable recovery(ε ≠ 0) or exact recovery(ε = 0) of all r-rank matrices X ∈ Rm×nfrom b = A(X) + z via nonconvex Schatten p-minimization for anyδ4r∈ [3~(1/2))2, 1). Moreover, we determine the range of parameter p with any given δ4r∈ [(3~(1/2))/22, 1). In fact, for any given δ4r∈ [3~(1/2))2, 1), p ∈(0, 2(1- δ4r)] suffices for the stable recovery or exact recovery of all r-rank matrices.
出处 《Science China Mathematics》 SCIE CSCD 2015年第12期2643-2654,共12页 中国科学:数学(英文版)
基金 supported by National Natural Science Foundation of China(Grant Nos.11271050 and 11371183) Beijing Center for Mathematics and Information Interdisciplinary Sciences
关键词 low-rank matrix recovery restricted isometry constant Schatten p-minimization Schatten k矩阵 稳定 非凸 充分条件 参数值
  • 相关文献

参考文献4

二级参考文献55

  • 1Argyriou A, Micchelli C, Pontil M. Convex multi-task feature learning. Machine Learning, 2008, 73:243-272.
  • 2Beck C, Andrea R. Computational study and comparisons of LFT reducibility methods. In: Proceedings of the American Control Conference. Michigan: American Automatic Control Council, 1998, 1013- 1017.
  • 3Cai J F, Cand's E J, Shen Z W. A singular value thresholding algorithm for matrix completion. SIAM J Optim, 2010, 20:1956-1982.
  • 4Cai T T, Wang L, Xu G W. Shifting inequality and recovery of sparse signals. IEEE Trans Signal Process, 2010, 58: 1300-1308.
  • 5Cai T T, Wang L, Xu G W. New bounds for restricted isometry constants. IEEE Trans Inform Theory, 2010, 56: 4388-4394.
  • 6Cand's E J. Compressive sampling. In: Proceedings of International Congress of Mathematicians, vol. 3. Madrid: European Mathematical Society Publishing House, 2006, 1433-1452.
  • 7Cand's E J. The restricted isometry property and its implications for compressed sensing. C R Acad Sci Paris Ser 1, 2008, 346:589 592.
  • 8Cand's E J, Plan Y. Tight oracle bounds for low-rank recovery from a minimal number of random measurements. IEEE Trans Inform Theory, 2009, 57:2342- 2359.
  • 9Cand's E J, Recht B. Exact matrix completion via convex optimization. Found Comput Math, 2008, 9:717-772.
  • 10Cand's E J, Tao T. Decoding by linear programming. IEEE Trans Inform Theory, 2005, 51:4203 -4215.

共引文献12

同被引文献5

引证文献3

二级引证文献7

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部