• DocumentCode
    229210
  • Title

    Unsupervised learning algorithm for signal separation

  • Author

    Jacob, Theju ; Snyder, Wesley

  • Author_Institution
    Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
  • fYear
    2014
  • fDate
    9-12 Dec. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    We present a neural network capable of separating inputs in an unsupervised manner. Oja´s rule and Self-Organizing map principles are used to construct the network. The network is tested using 1) straight lines 2)MNIST database. The results demonstrate that the network can operate as a general clustering algorithm, with neighboring neurons responding to geometrically similar inputs.
  • Keywords
    pattern clustering; self-organising feature maps; source separation; unsupervised learning; Oja rule; general clustering algorithm; neural network; neurons; self-organizing map principles; signal separation; unsupervised learning algorithm; Clustering algorithms; Hebbian theory; Lattices; Neurons; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), 2014 IEEE Symposium on
  • Conference_Location
    Orlando, FL
  • Type

    conf

  • DOI
    10.1109/CIMSIVP.2014.7013286
  • Filename
    7013286
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=229210