• DocumentCode
    1118536
  • Title

    Feature Extraction Using Problem Localization

  • Author

    Short, Robert D. ; Fukunaga, Keinosuke

  • Author_Institution
    Sperry Research Center, Sudbury, MA 01776.
  • Issue
    3
  • fYear
    1982
  • fDate
    5/1/1982 12:00:00 AM
  • Firstpage
    323
  • Lastpage
    326
  • Abstract
    Feature extraction is considered as a mean-quare estimation of the Bayes risk vector. The problem is simplified by partitioning the distribution space into local subregions and performing a linear estimation in each subregion. A modified clustering algorithm is used to fimd the partitioning which minimizes the mean-square error.
  • Keywords
    Artificial intelligence; Clustering algorithms; Cost function; Feature extraction; Nearest neighbor searches; Partitioning algorithms; Pattern recognition; Piecewise linear techniques; Vectors; Bayes risk; classification; feature extraction; piecewise linear features; problem localization;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1982.4767252
  • Filename
    4767252
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1118536