On geodesic exponential kernels

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We consider kernel methods on general geodesic metric spaces and provide both negative and positive results. First we show that the common Gaussian kernel can only be generalized to a positive definite kernel on a geodesic metric space if the space is flat. As a result, for data on a Riemannian manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness. This implies that geodesic Laplacian kernels can be generalized to some curved spaces, including spheres and hyperbolic spaces. Our theoretical results are verified empirically.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
EditorsAasa Feragen, Marcello Pelillo, Marco Loog
Number of pages3
PublisherSpringer
Publication date2015
Pages211-213
ISBN (Print)9781467369640
DOIs
Publication statusPublished - 2015
Event3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 - Copenhagen, Denmark
Duration: 12 Oct 201514 Oct 2015

Conference

Conference3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015
LandDenmark
ByCopenhagen
Periode12/10/201514/10/2015

ID: 160634529