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arXiv 2408.08233math.MGcs.LG
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Z-格罗莫夫-沃瑟斯坦距离

The Z-Gromov-Wasserstein Distance

Martin Bauer, Facundo Mémoli, Tom Needham, Mao Nishino

AI总结:

本文提出Z-格罗莫夫-沃瑟斯坦距离,作为度量测度空间的广义概念,统一了多种现有距离方法并保留了其关键性质。

摘要:

格罗莫夫-沃瑟斯坦(GW)距离是一种用于比较度量测度空间的强大工具,已在数据科学和机器学习中找到了广泛的应用。受分析具有日益复杂结构的数据集(如带有节点和边属性的图)的需求驱动,近年来文献中引入了GW距离的几种变体。为了建立GW类距离理论的一般框架,本文考虑了度量测度空间概念的广泛泛化:对于任意的度量空间Z,我们将一个Z-网络定义为一个配备了以Z为值的核的测度空间。我们通过定义一种广义的GW距离来比较Z-网络,这种距离我们称为Z-格罗莫夫-沃瑟斯坦(Z-GW)距离。这种构造包含了许多已知的度量,并提供了一种统一的方法来理解它们的共同特性。本文证明了Z-GW距离在Z-网络空间上定义了一个度量,保留了Z的有利性质,如分离性、完备性和测地性。许多这些性质对于现有属于我们框架的GW距离变体来说是未知的。我们的重点是基础理论,但我们的结果还包括可计算的下界和距离的近似值,这些对于实际应用也是有用的。

英文摘要:

The Gromov-Wasserstein (GW) distance is a powerful tool for comparing metric measure spaces which has found broad applications in data science and machine learning. Driven by the need to analyze datasets whose objects have increasingly complex structure (such as node and edge-attributed graphs), several variants of GW distance have been introduced in the recent literature. With a view toward establishing a general framework for the theory of GW-like distances, this paper considers a vast generalization of the notion of a metric measure space: for an arbitrary metric space $Z$, we define a $Z$-network to be a measure space endowed with a kernel valued in $Z$. We introduce a method for comparing $Z$-networks by defining a generalization of GW distance, which we refer to as $Z$-Gromov-Wasserstein ($Z$-GW) distance. This construction subsumes many previously known metrics and offers a unified approach to understanding their shared properties. This paper demonstrates that the $Z$-GW distance defines a metric on the space of $Z$-networks which retains desirable properties of $Z$, such as separability, completeness, and geodesicity. Many of these properties were unknown for existing variants of GW distance that fall under our framework. Our focus is on foundational theory, but our results also include computable lower bounds and approximations of the distance which will be useful for practical applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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