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Diffusion bases methods for segmentation and clustering

  • US 20080181503A1
  • Filed: 01/30/2007
  • Published: 07/31/2008
  • Est. Priority Date: 01/30/2007
  • Status: Active Grant
First Claim
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1. A method for dimensionality reduction of large data volumes comprising the steps of:

  • a. providing a dataset Γ

    of data points given as vectors;

    b. building a weighted graph G on Γ

    with a weight function wε

    , wherein wε

    corresponds to a local coordinate-wise similarity between the coordinates in Γ

    ;

    c. constructing a random walk on graph G via a Markov transition matrix P, which is derived from wε

    ;

    d. performing a spectral decomposition of P to obtain right and left eigenvectors of P; and

    e. projecting the data points in Γ

    onto the right eigenvectors of P to obtain a set of projection values Γ

    B for each data point, whereby Γ

    B represents coordinates in a reduced space.

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