用户名: 密码: 验证码:
Stable recovery of low-rank matrix via nonconvex Schatten p-minimization
详细信息    查看全文
  • 作者:WenGu Chen ; YaLing Li
  • 关键词:low ; rank matrix recovery ; restricted isometry constant ; Schatten p ; minimization ; 90C26 ; 90C59 ; 65F35
  • 刊名:SCIENCE CHINA Mathematics
  • 出版年:2015
  • 出版时间:December 2015
  • 年:2015
  • 卷:58
  • 期:12
  • 页码:2643-2654
  • 全文大小:211 KB
  • 参考文献:1.Amit Y, Fink M, Srebro N, et al. Uncovering shared structures in multiclass classification. In: Proceedings of the 24th International Conference on Machine Learning. New York: ACM, 2007, 17-4
    2.Basri R, Jacobs D W. Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell, 2003; 25: 218-33CrossRef
    3.Cai T T, Wang L, Xu G W. Shifting inequality and recovery of sparse signals. IEEE Trans Signal Process, 2010; 58: 1300-308MathSciNet CrossRef
    4.Cai T T, Wang L, Xu G W. New bounds for restricted isometry constants. IEEE Trans Inform Theory, 2010; 56: 4388-394MathSciNet CrossRef
    5.Cai T T, Zhang A. Sharp RIP bound for sparse signal and low-rank matrix recovery. Appl Comput Harmon Anal, 2013; 35: 74-3MATH MathSciNet CrossRef
    6.Cai T T, Zhang A. Compressed sensing and affine rank minimization under restricted isometry. IEEE Trans Signal Process, 2013; 61: 3279-290MathSciNet CrossRef
    7.Cai T T, Zhang A. Sparse representation of a polytope and recovery of sparse signals and low-rank matrices. IEEE Trans Inform Theory, 2014; 60: 122-32MathSciNet CrossRef
    8.Cai Y, Li S. Convergence analysis of projected gradient descent for Schatten-p nonconvex matrix recovery. Sci China Math, 2015; 58: 845-58MathSciNet CrossRef
    9.Candès E J. The restricted isometry property and its implications for compressed sensing. C R Marh Acad Sci Paris, 2008; 346: 589-92MATH CrossRef
    10.Candès E J, Plan Y. Tight oracle bounds for low-rank matrix recovery from a minimal number of random measurements. IEEE Trans Inform Theory, 2011; 57: 2342-359MathSciNet CrossRef
    11.Candès E J, Tao T. Decoding by linear programming. IEEE Trans Inform Theory, 2005; 51: 4203-215MATH MathSciNet CrossRef
    12.Chartrand R. Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal Process Lett, 2007; 14: 707-10CrossRef
    13.Chartrand R, Staneva V. Restricted isometry properties and nonconvex compressive sensing. Inverse Problems, 2008; 24: 1-4MathSciNet CrossRef
    14.Chen W G, Wu G Q. Harmonic analysis and research on the channel coding and decoding (in Chinese). Sci Sin Math, 2014; 44: 447-56CrossRef
    15.Dan W. Analysis of orthogonal multi-matching pursuit under restricted isometry property. Sci China Math, 2014; 57: 2179-188MATH MathSciNet CrossRef
    16.Dan W, Wang R H. Robustness of orthogonal matching pursuit under restricted isometry property. Sci China Math, 2014; 57: 627-34MATH MathSciNet CrossRef
    17.Dilworth S J. The dimension of Euclidean subspaces of quasi-normed spaces. Math Proc Cambridge Philos Soc, 1985; 97: 311-20MATH MathSciNet CrossRef
    18.Foucart S. A note on guaranteed sparse recovery via l1-minimization. Appl Comput Harmon Anal, 2010; 29: 97-03MATH MathSciNet CrossRef
    19.Foucart S. Sparse recovery algorithms: Sufficient conditions in terms of restricted isometry constants. Approximation Theory XIII: San Antonio 2010. New York: Springer, 2012: 65-7CrossRef
    20.Foucart S, Lai M J. Sparsest solutions of underdetermined linear systems via l q-minimization for 0 < q ?1. Appl Comput Harmon Anal, 2009; 26: 395-07MATH MathSciNet CrossRef
    21.Kong L C, Xiu N H. Exact low-rank matrix recovery via nonconvex Schatten p-minimization. Asia-Pac J Oper Res, 2013; 30: 1-3MATH CrossRef
    22.Lin J, Li S. Convergence of projected Landweber iteration for matrix rank minimization. Appl Comput Harmon Anal, 2014; 36: 316-25MATH MathSciNet CrossRef
    23.Liu L, Huang W, Chen D R. Exact minimum rank approximation via Schatten p-norm minimization. J Comput Appl Math, 2014; 267: 218-27MATH MathSciNet CrossRef
    24.Mo Q, Li S. New bounds on the restricted isometry constant d2k. Appl Comput Harmon Anal, 2011; 31: 460-68MATH MathSciNet CrossRef
    25.Mohan K, Fazel M. New restricted isometry results for noisy low-rank recovery. In: IEEE International Symposium on Information Theory Proceedings. Seattle: IEEE, 2010, 1573-577
    26.Recht B, Fazel M, Parrilo P A. Guaranteed minimum rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev, 2010; 52: 471-01MATH MathSciNet CrossRef
    27.Shen Y, Li S. Restricted p-isometry property and its application for nonconvex compressive sensing. Adv Comput Math, 2012; 37: 441-52MATH MathSciNet CrossRef
    28.Sun Q Y. Recovery of sparsest signals via lq-minimization. Appl Comput Harmon Anal, 2012; 32: 329-41MATH MathSciNet CrossRef
    29.Tomasi C, Kanade T. Shape and motion from image streams under orthography: A factorization method. Int J Comput Vis, 1992; 9: 137-54CrossRef
    30.Wang H M, Li S. The bounds of restricted isometry constants for low rank matrices recovery. Sci China Math, 2013; 56: 1117-127MATH MathSciNet CrossRef
    31.Wen J M, Li D F, Zhu F M. Stable recovery of sparse signals via lp-minimization. Appl Comput Harmon Anal, 2015; 38: 161-76MATH MathSciNet CrossRef
    32.Wu R, Chen D R. The improved bounds of restricted isometry constant for recovery
  • 作者单位:WenGu Chen (1)
    YaLing Li (2)

    1. Institute of Applied Physics and Computational Mathematics, Beijing, 100088, China
    2. Graduate School, China Academy of Engineering Physics, Beijing, 100088, China
  • 刊物类别:Mathematics and Statistics
  • 刊物主题:Mathematics
    Chinese Library of Science
    Applications of Mathematics
  • 出版者:Science China Press, co-published with Springer
  • ISSN:1869-1862
文摘
In this paper, a sufficient condition is obtained to ensure the stable recovery (? ?0) or exact recovery (? = 0) of all r-rank matrices X ??sup> m×n from \(b = \mathcal{A}(X) + z\) via nonconvex Schatten p-minimization for any \(\delta _{4r} \in \left[ {\frac{{\sqrt 3 }} {2},1} \right)\). Moreover, we determine the range of parameter p with any given δ\(\delta _{4r} \in \left[ {\frac{{\sqrt 3 }} {2},1} \right)\). In fact, for any given \(\delta _{4r} \in \left[ {\frac{{\sqrt 3 }} {2},1} \right)\), p ?(0, 2(1 ?δ4r)] suffices for the stable recovery or exact recovery of all r-rank matrices. Keywords low-rank matrix recovery restricted isometry constant Schatten p-minimization

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700