Information distance estimation between mixtures of multivariate Gaussians

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    There are efficient software programs for extracting from image sequences certain mixtures of
    distributions, such as multivariate Gaussians, to represent the important features needed for
    accurate document retrieval from databases. This note describes a method to use information
    geometric methods to measure distances between distributions in mixtures of multivariate
    Gaussians. There is no general analytic solution for the information geodesic distance between
    two k-variate Gaussians, but for many purposes the absolute information distance is
    not essential and comparative values suffice for proximity testing. For two mixtures of multivariate
    Gaussians we must resort to approximations to incorporate the weightings. In practice,
    the relation between a reasonable approximation and a true geodesic distance is likely to be
    monotonic, which is adequate for many applications. Here we compare several choices for
    the incorporation of weightings in distance estimation and provide illustrative results from
    simulations of differently weighted mixtures of multivariate Gaussians.
    Original languageEnglish
    Title of host publicationComputational Information Geometry: For Image And Signal Processing , Heidelberg 2017.
    Place of PublicationHeidelberg
    PublisherSpringer Nature
    Number of pages9
    Publication statusAccepted/In press - 2017

    Publication series

    NameSpringer Series in Signals and Communication Technology


    • Information geometry, multivariate spatial covariance, Gaussian mixtures, geodesic distance, approximations.


    Dive into the research topics of 'Information distance estimation between mixtures of multivariate Gaussians'. Together they form a unique fingerprint.

    Cite this