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arXiv 1806.090452018-06cs.CV

Variational Wasserstein Clustering

Arizona State University（亚利桑那州立大学）；Stony Brook University（石溪大学）

Liang Mi, Wen Zhang, Xianfeng Gu, Yalin Wang

备注：: Accepted to ECCV 2018

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

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