The Wasserstein Metric a.k.a Earth Mover's Distance: A Quick and Convenient Introduction
Here are two papers that describe this in more detail: Y. Lavin, R. Kumar Batra, and L. Hesselink. Feature Comparisons of Vector Fields Using Earth Mover’s Distance. In Proceedings IEEE Visualization ’98, pages 103–110, 1998. Y. Rubner, C. Tomasi, and L. J. Guibas. A Metric for Distributions with Applications to Image Databases. Computer Vision, 1998. Sixth International Conference on, 4-7 Jan 1998 Page(s): 59 - 66, 1998. Connect with Bob on LinkedIn: / robert-s-laramee Connect with Bob on Facebook: / datavisbob Connect with Bob on WeChat-User ID: rlaramee

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Glyph Design for Flow Visualization: A Quick and Convenient Summary

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Introduction to the Wasserstein distance

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Marco Cuturi - A Primer on Optimal Transport Part 1

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The Most Elegant Way to Compare Probability Distributions

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Estimating the Wasserstein Metric - Jonathan Niles-Weed

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Marco Cuturi - A Primer on Optimal Transport Part 2

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Shape Analysis (Lecture 19): Optimal transport

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An introduction to persistent homology

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Deep Learning 35: (2) Wasserstein Generative Adversarial Network (WGAN): Wasserstein metric

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Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport | Alex Tong

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A Near-Linear Time Algorithm for the Chamfer Distance

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Soheil Kolouri - Wasserstein Embeddings in the Deep Learning Era

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Optimal Transport and Information Geometry for Machine Learning and Data Science

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Wasserstein Distance & Optimal Transport — Fully Explained

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Marco Part A Primer on Optimal Transport Part 3

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