• DocumentCode
    2163438
  • Title

    Sampling on locally defined principal manifolds

  • Author

    Bas, Erhan ; Erdogmus, Deniz

  • Author_Institution
    ECE Dept., Northeastern Univ., Boston, MA, USA
  • fYear
    2011
  • fDate
    22-27 May 2011
  • Firstpage
    2276
  • Lastpage
    2279
  • Abstract
    We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the pdf. Proposed manifold score can be used to check if data pairs lie on the same manifold. We use this score to (i) cluster nonlinear manifolds having irregular shapes, and (ii) (down)sample a selected principal curve with sufficient accuracy sparsely. Our goal is to provide a heuristic-free formulation for principal graph generation and curve parametrization in order to form a basis for a principled principal manifold unwrapping method.
  • Keywords
    pattern clustering; probability; unsupervised learning; cluster nonlinear manifold; curve parametrization; graph generation; heuristic-free formulation; principal curve definition; probability density function; Eigenvalues and eigenfunctions; Euclidean distance; Image color analysis; Indexes; Kernel; Manifolds; Probability density function; Principal graphs; resampling on manifolds;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
  • Conference_Location
    Prague
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4577-0538-0
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2011.5946936
  • Filename
    5946936