• DocumentCode
    991256
  • Title

    Sparse component analysis and blind source separation of underdetermined mixtures

  • Author

    Georgiev, Pando ; Theis, Fabian ; Cichocki, Andrzej

  • Author_Institution
    Dept. of Electr. Comput. & Eng. Comput. Sci., Univ. of Cincinnati, OH, USA
  • Volume
    16
  • Issue
    4
  • fYear
    2005
  • fDate
    7/1/2005 12:00:00 AM
  • Firstpage
    992
  • Lastpage
    996
  • Abstract
    In this letter, we solve the problem of identifying matrices S ∈ Rn×N and A ∈ Rm×n knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.
  • Keywords
    blind source separation; identification; sparse matrices; blind source separation; identifiability condition; matrix identification; sparse component analysis; underdetermined mixtures; Blind source separation; Data analysis; Data mining; Dictionaries; Independent component analysis; Neuroscience; Random variables; Signal processing algorithms; Source separation; Sparse matrices; Blind source separation (BSS); sparse component analysis (SCA); underdetermined mixtures; Algorithms; Artificial Intelligence; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2005.849840
  • Filename
    1461442
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=991256