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arXiv 2602.23154math.ATmath.OC
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持久拓扑特征的鲁棒性与最小同调切割

Robustness of Persistent Topological Features and Minimum Homological Cuts

KTH Stockholm(斯德哥尔摩皇家理工学院);University of Amsterdam(阿姆斯特丹大学);ETH Zurich(苏黎世联邦理工学院)

Pepijn Roos Hoefgeest, Lucas Slot

AI总结:

本文研究了持久同调中特征的对抗鲁棒性,提出了一种基于同调的最小切割问题,并提供了计算复杂性结果和启发法方法。

摘要:

持久同调是一种计算度量数据拓扑特征的流行方法。基于Čech或Rips过滤的标准方法在数据的小扰动下是稳定的,但对异常值高度敏感。这种缺乏鲁棒性的问题在文献中经常被讨论。在本文中,我们从一个新的角度出发,提出了以下问题:在有限数量的未知任意异常值存在的情况下,何时可以保证观察到的持久特征(一个条形)是底层数据固有的。我们通过引入“对抗鲁棒性”的概念来正式化这个问题,并研究决定给定过滤 simplicial 复合体的条形码中的条形是否对抗鲁棒的问题。我们证明这个问题本质上等同于 simplicial 复合体中的最小切割问题的同调变种,这我们认为具有独立的兴趣。作为主要技术贡献,我们提供了该问题的第一个计算复杂性结果,包括0维同调中的高效算法、一般问题的NP难性,以及在n维复合体中嵌入R^n中的codimension-1的高效算法。我们还分析了其自然的线性规划松弛,其对偶定义了图中的max-flow问题的同调类比。我们证明在我们的设定中max-flow/min-cut定理不成立,这意味着线性规划松弛在一般情况下不紧。最后,在Rips过滤的特殊情况下,我们提供了一个基于Hausdorff距离的全局启发法,该方法保证了足够长的条形的对抗鲁棒性。这将对抗鲁棒性与持久同调中的标准稳定性定理联系起来。

英文摘要:

Persistent homology is a popular method for computing topological features of (metric) data. Standard approaches based on the Čech or Rips filtration are stable under small perturbations of the data, but highly sensitive to outliers. This lack of robustness has been frequently addressed in the literature. In this paper, we take a novel perspective by asking the following question: When can we guarantee that an observed persistent feature (a bar) is inherent to the underlying data in the presence of a limited number of unknown, arbitrary outliers. We formalize this question by introducing the notion of \emph{adversarial robustness}, and study the problem of deciding whether a given bar in the barcode of a filtered simplicial complex is adversarially robust. We show that this problem is essentially equivalent to a homological variant of the minimum cut problem in simplicial complexes, which we believe to be of independent interest. As our main technical contribution, we provide the first computational complexity results for this problem, consisting of an efficient algorithm in $0$-dimensional homology, NP-hardness for the general problem, and an efficient algorithm for codimension-$1$ in $n$-dimensional complexes embedded in $\mathbb{R}^n$. We also analyze its natural linear programming relaxation, whose dual defines a homological analog of the max-flow problem in graphs. We show that a max-flow/min-cut theorem does not hold in our setting, implying that the LP relaxation is not tight in general. Finally, in the special case of the Rips filtration, we provide a global heuristic based on the Hausdorff distance that guarantees adversarial robustness of sufficiently long bars. This connects adversarial robustness to standard stability theorems in persistent homology.

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