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arXiv 1908.00063cs.CGmath.AT

内在交织距离用于合并树

Intrinsic Interleaving Distance for Merge Trees

Australian National University(澳大利亚国立大学);University of Utah(犹他大学);Ohio State University(俄亥俄州立大学);Union College(联合学院);Michigan State University(密歇根州立大学);Inria Saclay(法国国家信息与自动化研究所萨克莱中心)

Ellen Gasparovic, Elizabeth Munch, Steve Oudot, Katharine Turner, Bei Wang, Yusu Wang

AI总结:

本文研究了通过度量空间中的交织距离比较两个合并树的问题,证明了交织距离在有标签和无标签合并树空间中的内在性,并提出构造度量1中心的算法。

摘要:

合并树是一种基于图的拓扑总结,用于跟踪标量函数子水平集连通分量的演变。本文考虑了通过度量空间中的交织距离比较两个合并树的问题。我们研究了此类度量的理论性质,特别是证明了交织距离在有标签合并树空间中的内在性,并提出构造度量1中心的算法。我们进一步证明,交织距离的内在性也适用于无标签合并树空间。我们的结果是进行基于图的拓扑总结统计学研究的第一步。

英文摘要:

Merge trees are a type of graph-based topological summary that tracks the evolution of connected components in the sublevel sets of scalar functions. They enjoy widespread applications in data analysis and scientific visualization. In this paper, we consider the problem of comparing two merge trees via the notion of interleaving distance in the metric space setting. We investigate various theoretical properties of such a metric. In particular, we show that the interleaving distance is intrinsic on the space of labeled merge trees and provide an algorithm to construct metric 1-centers for collections of labeled merge trees. We further prove that the intrinsic property of the interleaving distance also holds for the space of unlabeled merge trees. Our results are a first step toward performing statistics on graph-based topological summaries.

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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