• DocumentCode
    2207399
  • Title

    Estimation of density ratio and its application to design a measure of dependence

  • Author

    Seth, Sohan ; Príncipe, José C.

  • Author_Institution
    Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
  • fYear
    2009
  • fDate
    1-4 Sept. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper we propose a new approach to estimate the ratio of two probability density functions. The proposed approach is inspired by the kernel based function approximation technique. We apply this estimator to derive an estimator of mutual information and show that this estimator can be successfully used to detect dependence between two random variables.
  • Keywords
    learning (artificial intelligence); probability; density ratio estimation; dependence measure; function approximation technique; mutual information; probability density functions; random variables; Application software; Density functional theory; Density measurement; Design engineering; Electric variables measurement; Gain measurement; Kernel; Mutual information; Probability density function; Random variables;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
  • Conference_Location
    Grenoble
  • Print_ISBN
    978-1-4244-4947-7
  • Electronic_ISBN
    978-1-4244-4948-4
  • Type

    conf

  • DOI
    10.1109/MLSP.2009.5306226
  • Filename
    5306226
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2207399