• DocumentCode
    2714577
  • Title

    Dictionary Learning Research Based on Sparse Representation

  • Author

    Song, Lijuan ; Peng, Jinye

  • Author_Institution
    Sch. of Inf. Sci. & Technol., Northwest Univ., Xi´´an, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    14
  • Lastpage
    17
  • Abstract
    Although the number of representation subspaces is large, only few ones will contain data samples from sensor measurements. By identifying these few subspaces, we find the representation in the reduced space. We describe sparse representation and dictionary learning methods. Finally there is the experiment of the application of dictionary learning. The main focus of this thesis is the dictionary learning theory and experimental results show that the algorithm is effective.
  • Keywords
    dictionaries; image representation; learning (artificial intelligence); data samples; dictionary learning methods; dictionary learning research; sensor measurements; sparse signal representation; Approximation algorithms; Approximation methods; Dictionaries; Educational institutions; Learning systems; Matching pursuit algorithms; Vectors; dictionary learning; sparse representation; trained dictionary;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.12
  • Filename
    6394250