On the Universality of Random Persistence Diagrams
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One of the most elusive challenges within the area of topological data analysis is understanding the distribution of persistence diagrams. Despite much effort, this is still largely an open problem. In this paper, we present a series of novel conjectures regarding the behavior of persistence diagrams arising from random point-clouds. We claim that these diagrams obey a universal probability law, and include an explicit expression as a candidate for what this law is. We back these conjectures with an exhaustive set of experiments, including both simulated and real data. We demonstrate the power of these conjectures by proposing a new hypothesis testing framework for individual features within persistence diagrams.
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Cited by 1 Pith paper
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From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
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