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arXiv 1004.4753math.ATcs.CG
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持久拓扑与同调中多维匹配距离的不变性

Invariance properties of the multidimensional matching distance in Persistent Topology and Homology

Andrea Cerri, Patrizio Frosini

摘要:

Persistent Topology通过分析合适函数的低层集合(称为过滤函数)来研究形状的拓扑特征,并将所产生的信息编码到参数化的贝蒂数中,即持久同调群的秩中。最初介绍考虑实值过滤功能,持久拓扑已随后推广到多维设置,即$\R^n$值过滤功能的情况下,导致研究的行列多维同调群。特别是,已经定义了多维匹配距离,以比较这些排名。多维匹配距离的定义是基于通过半平面的集合来划分多维同调群的秩的域,因此它形式上依赖于诱导这些半平面的参数化的$\R^n\times\R^n$的子集。碰巧的是,有可能以无数种不同的方式选择这个子集。在本文中,我们表明,多维匹配距离实际上是不变的,这样的选择。

英文摘要:

Persistent Topology studies topological features of shapes by analyzing the lower level sets of suitable functions, called filtering functions, and encoding the arising information in a parameterized version of the Betti numbers, i.e. the ranks of persistent homology groups. Initially introduced by considering real-valued filtering functions, Persistent Topology has been subsequently generalized to a multidimensional setting, i.e. to the case of $\R^n$-valued filtering functions, leading to studying the ranks of multidimensional homology groups. In particular, a multidimensional matching distance has been defined, in order to compare these ranks. The definition of the multidimensional matching distance is based on foliating the domain of the ranks of multidimensional homology groups by a collection of half-planes, and hence it formally depends on a subset of $\R^n\times\R^n$ inducing a parameterization of these half-planes. It happens that it is possible to choose this subset in an infinite number of different ways. In this paper we show that the multidimensional matching distance is actually invariant with respect to such a choice.

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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