• DocumentCode
    1077816
  • Title

    Random Projection Trees for Vector Quantization

  • Author

    Dasgupta, Sanjoy ; Freund, Yoav

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of California, La Jolla, CA
  • Volume
    55
  • Issue
    7
  • fYear
    2009
  • fDate
    7/1/2009 12:00:00 AM
  • Firstpage
    3229
  • Lastpage
    3242
  • 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; quantization error; random projection trees; tree-structured vector quantization; Algorithm design and analysis; Computer science; Euclidean distance; Machine learning; Manifolds; Partitioning algorithms; Source coding; Statistical analysis; Statistics; Vector quantization; Computational complexity; manifolds; random projection; source coding; vector quantization;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2009.2021326
  • Filename
    5075899
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1077816