• DocumentCode
    3456366
  • Title

    A Semi-Definite Programming Embedding Framework for Local Preserving Manifold Learning

  • Author

    Zeng, Xianhua ; Gan, Ling ; Wang, Jian

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., Chongqing, China
  • fYear
    2010
  • fDate
    21-23 Oct. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    A semi-definite programming embedding framework is presented for local preserving manifold learning in this paper. Under the framework, three unstable algorithms (LE, LLE and LTSA) are respectively converted into the stable semi-definite programming embedding algorithms (named as SDPE-LE, SDPE-LLE and SDPE-LTSA). The advantages and effectiveness of these new algorithms are demonstrated via the experimental results on synthetic dataset and real image dataset.
  • Keywords
    learning (artificial intelligence); nonlinear programming; visual databases; local preserving manifold learning; real image dataset; semidefinite programming embedding framework; synthetic dataset; unstable algorithms; Laplace equations; Manifolds; Optimization methods; Programming; Software; Telecommunications;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (CCPR), 2010 Chinese Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-7209-3
  • Electronic_ISBN
    978-1-4244-7210-9
  • Type

    conf

  • DOI
    10.1109/CCPR.2010.5659162
  • Filename
    5659162
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3456366