• DocumentCode
    256695
  • Title

    Landmark-Based Local Patches Alignment Embedding

  • Author

    Jing Chen ; Yang Liu

  • Author_Institution
    Sch. of Phys. & Optoelectron. Eng., Guangdong Univ. of Technol., Guangzhou, China
  • Volume
    2
  • fYear
    2014
  • fDate
    26-27 Aug. 2014
  • Firstpage
    104
  • Lastpage
    107
  • Abstract
    A novel embedding method, called landmark-based local patches alignment embedding (LLPA), is proposed. LLPA first searches a set of landmarks which preserve the global structure of data set well and constructs overlapping patches based on these landmarks. Then, global isometric mapping and multidimensional scale are applied respectively to derive the low-dimensional coordinates of the landmarks and local patches. Finally, we yield the resulting global coordinates by patches alignment technique combined with a set of landmarks in low-dimensional space as reference points.
  • Keywords
    learning (artificial intelligence); LLPA embedding method; global isometric mapping; landmark coordinates; landmark-based local patches alignment embedding; low-dimensional space; manifold learning; multidimensional scale; patches alignment technique; Artificial intelligence; Cybernetics; Educational institutions; Laplace equations; Manifolds; Pattern recognition; Vectors; Manifold learning; local tangent space alignment; multidimensional scale;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-4956-4
  • Type

    conf

  • DOI
    10.1109/IHMSC.2014.128
  • Filename
    6911459