• DocumentCode
    2185758
  • Title

    Low-rank matrix recovery from non-linear observations

  • Author

    Bhattacharjee, Protim ; Khurana, Prerna ; Majumdar, Angshul

  • Author_Institution
    IIIT-Delhi, India
  • fYear
    2015
  • fDate
    21-24 July 2015
  • Firstpage
    623
  • Lastpage
    627
  • Abstract
    Algorithms for sparse recovery problems from non-linear measurements have attracted some attention lately. Closely related to the problem of sparse is recovery is the problem of low-rank matrix recovery. There is no work on the topic of low-rank matrix recovery from non-linear measurements. This is the first study that proposes two algorithms for the said problem. The first one is based on nuclear norm minimization while the second one is based on Ky-Fan norm minimization.
  • Keywords
    Inverse problems; Magnetic resonance imaging; Matrix decomposition; Minimization; Protons; Signal processing algorithms; Sparse matrices; Ky-Fan norm; matrix completion; non-linear inverse problem; nuclear norm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Signal Processing (DSP), 2015 IEEE International Conference on
  • Conference_Location
    Singapore, Singapore
  • Type

    conf

  • DOI
    10.1109/ICDSP.2015.7251949
  • Filename
    7251949