Information distance estimation between mixtures of multivariate Gaussians

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Bibliographical metadata

Original languageEnglish
Title of host publicationComputational Information Geometry: For Image And Signal Processing , Heidelberg 2017.
Place of PublicationHeidelberg
PublisherSpringer
Number of pages9
StateAccepted/In press - 2017

Publication series

NameSpringer Series in Signals and Communication Technology