A Density-ratio Framework for Statistical Data Processing

Access this Article

Search this Article

Author(s)

Abstract

In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea — known as Vapnik's principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a lot of attention in the machine learning and data mining communities. The purpose of this paper is to introduce to the computer vision community recent advances in density ratio estimation methods and their usage in various statistical data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and independent component analysis.

Journal

  • IPSJ Transactions on Computer Vision and Applications

    IPSJ Transactions on Computer Vision and Applications 1, 183-208, 2009

    Information Processing Society of Japan

Cited by:  9

Codes

  • NII Article ID (NAID)
    130000123068
  • NII NACSIS-CAT ID (NCID)
    AN00116647
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    1882-7772
  • NDL Article ID
    024319661
  • NDL Call No.
    YH247-812
  • Data Source
    CJPref  NDL  J-STAGE 
Page Top