Abstract Optimal Transport (OT) distances result in a powerful technique to compare the probability distributions. Defining a similarity measure between clusters has been an open problem in Statistics. This paper introduces a hierarchical clustering algorithm using the OT based distance measures and analyzes the performance of the proposed algorithm on standard datasets with respect to the existing and popular hierarchical clustering methods.

中文翻译：

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具有最优传输的分层聚类

摘要 最佳传输 (OT) 距离是一种比较概率分布的强大技术。定义聚类之间的相似性度量一直是统计学中的一个悬而未决的问题。本文介绍了一种使用基于 OT 的距离度量的层次聚类算法，并分析了所提出的算法在标准数据集上相对于现有和流行的层次聚类方法的性能。