• DocumentCode
    3089899
  • Title

    Kernel Sammon Map

  • Author

    Inaba, Fernando K. ; Salles, Evandro O T ; Rauber, Thomas W.

  • Author_Institution
    Dept. de Eng. Eletr., Univ. Fed. do Espirito Santo, Vitoria, Brazil
  • fYear
    2011
  • fDate
    28-31 Aug. 2011
  • Firstpage
    329
  • Lastpage
    336
  • Abstract
    We extend the visualization technique of high-dimensional patterns conceived by Sammon to the case when the patterns have been previously mapped to an implicitly defined Hilbert feature space in which distances can be measured by kernels. The principal benefit of our technique is the possibility to gain insight into the distribution of the patterns, even in this generally non-accessible feature space.
  • Keywords
    Hilbert spaces; data visualisation; pattern recognition; principal component analysis; Hilbert feature space; high-dimensional pattern visualization technique; kernel Sammon map; nonaccessible feature space; pattern distribution; Hilbert space; Interpolation; Kernel; Stress; Training; Vectors; Visualization; Hilbert feature space; Kernel Principal Component Analysis; Kernels; Sammon map; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Graphics, Patterns and Images (Sibgrapi), 2011 24th SIBGRAPI Conference on
  • Conference_Location
    Maceio, Alagoas
  • Print_ISBN
    978-1-4577-1674-4
  • Type

    conf

  • DOI
    10.1109/SIBGRAPI.2011.22
  • Filename
    6134767
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3089899