Evolution equations with anisotropic distributions and diffusion PCA

4 Citations (Scopus)

Abstract

This paper presents derivations of evolution equations for the family of paths that in the Diffusion PCA framework are used for approximating data likelihood. The paths that are formally interpreted as most probable paths generalize geodesics in extremizing an energy functional on the space of differentiable curves on a manifold with connection. We discuss how the paths arise as projections of geodesics for a (non bracket-generating) sub-Riemannian metric on the frame bundle. Evolution equations in coordinates for both metric and cometric formulations of the sub-Riemannian geometry are derived. We furthermore show how rank-deficient metrics can be mixed with an underlying Riemannian metric, and we use the construction to show how the evolution equations can be implemented on finite dimensional LDDMM landmark manifolds.

Original languageEnglish
Title of host publicationGeometric science of information : Second International Conference, GSI 2015, Palaiseau, France, October 28–30, 2015, Proceedings
EditorsFrank Nielsen, Frédéric Barbaresco
Number of pages9
PublisherSpringer Science+Business Media
Publication date2015
Pages3-11
ISBN (Print)978-3-319-25039-7
ISBN (Electronic)978-3-319-25040-3
DOIs
Publication statusPublished - 2015
EventInternational Conference, GSI 2015 - Palaiseau, France
Duration: 28 Oct 201530 Oct 2015
Conference number: 2

Conference

ConferenceInternational Conference, GSI 2015
Number2
Country/TerritoryFrance
CityPalaiseau
Period28/10/201530/10/2015
SeriesLecture notes in computer science
Volume9389
ISSN0302-9743

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