• DocumentCode
    3423429
  • Title

    Principal tangent data reduction

  • Author

    Hunt, Thomas ; Krener, Arthur J.

  • Author_Institution
    Dept. of Math., Univ. of California, Davis, CA, USA
  • fYear
    2009
  • fDate
    9-11 Dec. 2009
  • Firstpage
    1577
  • Lastpage
    1580
  • Abstract
    There is a need to be able to find patterns in high dimensional data sets. Often these patterns are described as lower dimensional manifolds possibly of varying dimension that more or less fit the data. We present a new algorithm for doing this. It is a form of nonlinear principle component analysis.
  • Keywords
    data reduction; principal component analysis; high dimensional data sets; lower dimensional manifolds; nonlinear principle component analysis; principal tangent data reduction; Automatic control; Automation; Covariance matrix; Eigenvalues and eigenfunctions; Mathematics; Piecewise linear techniques; Principal component analysis; Sun; USA Councils; Nonlinear dimension reduction; manifold learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Automation, 2009. ICCA 2009. IEEE International Conference on
  • Conference_Location
    Christchurch
  • Print_ISBN
    978-1-4244-4706-0
  • Electronic_ISBN
    978-1-4244-4707-7
  • Type

    conf

  • DOI
    10.1109/ICCA.2009.5410162
  • Filename
    5410162
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3423429