EMD
Code for the Earth Movers Distance (EMD). This is an implementation of the Earth Movers Distance, as described in [1]. The EMD computes the distance between two distributions, which are represented by signatures. The signatures are sets of weighted features that capture the distributions. The features can be of any type and in any number of dimensions, and are defined by the user. The EMD is defined as the minimum amount of work needed to change one signature into the other. The notion of ”work” is based on the user-defined ground distance which is the distance between two features. The size of the two signatures can be different. Also, the sum of weights of one signature can be different than the sum of weights of the other (partial match). Because of this, the EMD is normalized by the smaller sum. The code is implemented in C, and is based on the solution for the Transportation problem as described in [2] Please let me know of any bugs you find, or any questions, comments, suggestions, and criticisms you have. If you find this code useful for your work, I would like very much to hear from you. Once you do, I’ll inform you of any improvements, etc. Also, an acknowledgment in any publication describing work that uses this code would be greatly appreciated.
Keywords for this software
References in zbMATH (referenced in 204 articles , 1 standard article )
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Sorted by year (- Balzanella, Antonio; Irpino, Antonio: Spatial prediction and spatial dependence monitoring on georeferenced data streams (2020)
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- Métivier, Ludovic; Brossier, R.; Mérigot, Q.; Oudet, E.: A graph space optimal transport distance as a generalization of (L^p) distances: application to a seismic imaging inverse problem (2019)
- Ostrovska, Sofiya; Ostrovskii, Mikhail I.: Generalized transportation cost spaces (2019)
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- Sommerfeld, Max; Schrieber, Jörn; Zemel, Yoav; Munk, Axel: Optimal transport: fast probabilistic approximation with exact solvers (2019)
- Tameling, Carla; Sommerfeld, Max; Munk, Axel: Empirical optimal transport on countable metric spaces: distributional limits and statistical applications (2019)
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