Least-Squares Conditional Density Estimation

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Author(s)

Abstract

Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.

Journal

  • IEICE Transactions on Information and Systems

    IEICE Transactions on Information and Systems 93(3), 583-594, 2010-03-01

    The Institute of Electronics, Information and Communication Engineers

References:  49

Cited by:  3

Codes

  • NII Article ID (NAID)
    10026814428
  • NII NACSIS-CAT ID (NCID)
    AA10826272
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    09168532
  • Data Source
    CJP  CJPref  J-STAGE 
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