• DocumentCode
    2485857
  • Title

    Local and Global Structures Preserving Projection

  • Author

    Cheng, Hao ; Hua, Kien A. ; Vu, Khanh

  • Author_Institution
    Univ. of Central Florida, Orlando
  • Volume
    2
  • fYear
    2007
  • fDate
    29-31 Oct. 2007
  • Firstpage
    362
  • Lastpage
    365
  • Abstract
    In this paper, we propose Local and Global Structures Preserving Projection (LGSPP), which is to find a small set of projection directions so as to properly preserve the local and global structures for a given set of data. Specifically, for each point in the dataset, its local neighborhood is extracted as well as a set of sampled points far away from this point, which characterize the global structure. The embedding minimizes the distances of the points in each local neighborhood while dispersing them far apart from their corresponding remote points. In this way, the local-global relationships between data points are well kept.
  • Keywords
    learning (artificial intelligence); global structures preserving projection; local neighborhood; local structures preserving projection; manifold learning; Artificial intelligence; Computer science; Data mining; Euclidean distance; Large-scale systems; Nearest neighbor searches; Nonlinear distortion; Principal component analysis; Proposals; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
  • Conference_Location
    Patras
  • ISSN
    1082-3409
  • Print_ISBN
    978-0-7695-3015-4
  • Type

    conf

  • DOI
    10.1109/ICTAI.2007.145
  • Filename
    4410406