Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification-Reference-Cited by-同舟云学术

Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

Published:2023-02-15 Issue:4 Volume:32 Page:1512-1523
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Li Mengyu¹^ORCID,Yu Jun²^ORCID,Xu Hongteng³^ORCID,Meng Cheng⁴^ORCID

Affiliation:

1. Institute of Statistics and Big Data, Renmin University of China, Beijing, China

2. School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China

3. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

4. Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China

Funder

Outstanding Innovative Talents Cultivation

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2165500

Reference70 articles.

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2. Alvarez-Melis D. and Jaakkola T. (2018) “Gromov-Wasserstein Alignment of Word Embedding Spaces ” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing pp. 1881–1890 Brussels Belgium. ACL.

3. The Monge-Kantorovitch mass transfer and its computational fluid mechanics formulation

4. Blumberg A. J. Carriere M. Mandell M. A. Rabadan R. and Villar S. (2020) “MREC: A Fast and Versatile Framework for Aligning and Matching Point Clouds with Applications to Single Cell Molecular Data ” arXiv preprint arXiv:2001.01666.

5. Sliced and Radon Wasserstein Barycenters of Measures

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