• DocumentCode
    2944501
  • Title

    Random projection trees for vector quantization

  • Author

    Dasgupta, Sanjoy ; Freund, Yoav

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, CA
  • fYear
    2008
  • fDate
    23-26 Sept. 2008
  • Firstpage
    192
  • Lastpage
    197
  • Abstract
    A simple and computationally efficient scheme for tree-structured vector quantization is presented. Unlike previous methods, its quantization error depends only on the intrinsic dimension of the data distribution, rather than the apparent dimension of the space in which the data happen to lie.
  • Keywords
    trees (mathematics); vector quantisation; data distribution; random projection trees; vector quantization; Computational complexity; Computer science; Error analysis; Machine learning; Manifolds; Power engineering and energy; Source coding; Statistical analysis; Statistics; Vector quantization; Vector quantization; computational complexity; manifolds; random projection; source coding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication, Control, and Computing, 2008 46th Annual Allerton Conference on
  • Conference_Location
    Urbana-Champaign, IL
  • Print_ISBN
    978-1-4244-2925-7
  • Electronic_ISBN
    978-1-4244-2926-4
  • Type

    conf

  • DOI
    10.1109/ALLERTON.2008.4797555
  • Filename
    4797555
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2944501