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Gromov-Wasserstein Averaging in a Riemannian Framework
lib:2da08329c4fa44a5 (v1.0.0)
Authors: Samir Chowdhury,Tom Needham
ArXiv: 1910.04308
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/1910.04308v2
We introduce a theoretical framework for performing statistical tasks---including, but not limited to, averaging and principal component analysis---on the space of (possibly asymmetric) matrices with arbitrary entries and sizes. This is carried out under the lens of the Gromov-Wasserstein (GW) distance, and our methods translate the Riemannian framework of GW distances developed by Sturm into practical, implementable tools for network data analysis. Our methods are illustrated on datasets of letter graphs, asymmetric stochastic blockmodel networks, and planar shapes viewed as metric spaces. On the theoretical front, we supplement the work of Sturm by producing additional results on the tangent structure of this "space of spaces", as well as on the gradient flow of the Fr\'{e}chet functional on this space.
ArXiv: 1910.04308
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/1910.04308v2
We introduce a theoretical framework for performing statistical tasks---including, but not limited to, averaging and principal component analysis---on the space of (possibly asymmetric) matrices with arbitrary entries and sizes. This is carried out under the lens of the Gromov-Wasserstein (GW) distance, and our methods translate the Riemannian framework of GW distances developed by Sturm into practical, implementable tools for network data analysis. Our methods are illustrated on datasets of letter graphs, asymmetric stochastic blockmodel networks, and planar shapes viewed as metric spaces. On the theoretical front, we supplement the work of Sturm by producing additional results on the tangent structure of this "space of spaces", as well as on the gradient flow of the Fr\'{e}chet functional on this space.
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