• DocumentCode
    2133333
  • Title

    Kernel entropy component analysis: New theory and semi-supervised learning

  • Author

    Jenssen, Robert

  • Author_Institution
    Dept. of Phys. & Technol., Univ. of Tromso, Tromso, Norway
  • fYear
    2011
  • fDate
    18-21 Sept. 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    A new theory for kernel entropy component analysis (kernel ECA) is developed, based on distribution dependent convolution operators, ensuring the validity of the method for any positive semi-definite kernel. Furthermore, a new semi-supervised kernel ECA classification method is derived with positive results compared to the state-of-the-art.
  • Keywords
    entropy; learning (artificial intelligence); pattern classification; principal component analysis; ECA classification method; distribution dependent convolution operators; kernel entropy component analysis; semisupervised learning; Convolution; Eigenvalues and eigenfunctions; Entropy; Indexes; Kernel; Principal component analysis; Vectors; Kernel entropy component analysis; classification; convolution operators; semi-supervised; spectral;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
  • Conference_Location
    Santander
  • ISSN
    1551-2541
  • Print_ISBN
    978-1-4577-1621-8
  • Electronic_ISBN
    1551-2541
  • Type

    conf

  • DOI
    10.1109/MLSP.2011.6064626
  • Filename
    6064626
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2133333