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2018 | OriginalPaper | Chapter

Variational Wasserstein Clustering

Authors : Liang Mi, Wen Zhang, Xianfeng Gu, Yalin Wang

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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Abstract

We propose a new clustering method based on optimal transportation. We discuss the connection between optimal transportation and k-means clustering, solve optimal transportation with the variational principle, and investigate the use of power diagrams as transportation plans for aggregating arbitrary domains into a fixed number of clusters. We drive cluster centroids through the target domain while maintaining the minimum clustering energy by adjusting the power diagram. Thus, we simultaneously pursue clustering and the Wasserstein distance between the centroids and the target domain, resulting in a measure-preserving mapping. We demonstrate the use of our method in domain adaptation, remeshing, and learning representations on synthetic and real data.

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Metadata
Title
Variational Wasserstein Clustering
Authors
Liang Mi
Wen Zhang
Xianfeng Gu
Yalin Wang
Copyright Year
2018
Book
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01266-3

Electronic ISBN: 978-3-030-01267-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01267-0_20

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