Quantized Gromov-Wasserstein

1 code implementation • 5 Apr 2021 • Samir Chowdhury, David Miller, Tom Needham

The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces.

Quantization

Statistical Shape Analysis of Brain Arterial Networks (BAN)

no code implementations • 8 Jul 2020 • Xiaoyang Guo, Aditi Basu Bal, Tom Needham, Anuj Srivastava

This framework is then used to generate shape summaries of BANs from 92 subjects, and to study the effects of age and gender on shapes of BAN components.

Generalized Spectral Clustering via Gromov-Wasserstein Learning

1 code implementation • 7 Jun 2020 • Samir Chowdhury, Tom Needham

A key insight of the GWL framework toward graph partitioning was to compute GW correspondences from a source graph to a template graph with isolated, self-connected nodes.

Clustering graph partitioning +1

Gromov-Wasserstein Averaging in a Riemannian Framework

1 code implementation • 10 Oct 2019 • Samir Chowdhury, Tom Needham

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.

Shape analysis of framed space curves

no code implementations • 10 Jul 2018 • Tom Needham

In this paper, we extend this shape analysis framework to treat shapes of framed space curves.