• DocumentCode
    3511520
  • Title

    Explicit matrices for sparse approximation

  • Author

    Khajehnejad, Amin ; Tehrani, Arash Saber ; Dimakis, Alexandros G. ; Hassibi, Babak

  • Author_Institution
    Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    469
  • Lastpage
    473
  • Abstract
    We show that girth can be used to certify that sparse compressed sensing matrices have good sparse approximation guarantees. This allows us to present the first deterministic measurement matrix constructions that have an optimal number of measurements for ℓ1/ℓ1 approximation. Our techniques are coding theoretic and rely on a recent connection of compressed sensing to LP relaxations for channel decoding.
  • Keywords
    approximation theory; channel coding; decoding; sparse matrices; LP relaxations; channel decoding; explicit matrices; first deterministic measurement matrix constructions; sparse approximation; sparse compressed sensing matrices; Approximation methods; Compressed sensing; Decoding; Parity check codes; Sparse matrices; Symmetric matrices; USA Councils;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
  • Conference_Location
    St. Petersburg
  • ISSN
    2157-8095
  • Print_ISBN
    978-1-4577-0596-0
  • Electronic_ISBN
    2157-8095
  • Type

    conf

  • DOI
    10.1109/ISIT.2011.6034170
  • Filename
    6034170
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3511520