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arxiv: 1510.02502 · v1 · pith:PWJT57BG · submitted 2015-10-08 · 📊 stat.ME · stat.ML

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Statistical Analysis of Persistence Intensity Functions

classification 📊 stat.ME stat.ML
keywords persistencediagramsintensitythemanalysisapproachfunctionfunctions
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Persistence diagrams are two-dimensional plots that summarize the topological features of functions and are an important part of topological data analysis. A problem that has received much attention is how deal with sets of persistence diagrams. How do we summarize them, average them or cluster them? One approach -- the persistence intensity function -- was introduced informally by Edelsbrunner, Ivanov, and Karasev (2012). Here we provide a modification and formalization of this approach. Using the persistence intensity function, we can visualize multiple diagrams, perform clustering and conduct two-sample tests.

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  1. A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification

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    PLACE delivers a closed-form persistent-homology classifier for point clouds and graphs with explicit margin bounds, descriptor selection, and training-time certificates derived solely from labels.