Universal Mappings and Analysis of Functional Data on Geometric Domains
Soheil Anbouhi, Washington Mio, Osman Berat Okutan

TL;DR
This paper develops metrics for analyzing functional data on geometric domains, investigates empirical estimation of these metrics, and constructs a universal Lipschitz function space encompassing all such functions on Polish metric spaces.
Contribution
It introduces new metrics for functional data on metric spaces, explores their empirical estimation, and constructs a universal Lipschitz function space for all functions on Polish metric spaces.
Findings
Proposed metrics for functional data on geometric domains.
Empirical methods for estimating these metrics.
Constructed a universal Lipschitz function space.
Abstract
This paper addresses problems in functional metric geometry that arise in the study of data such as signals recorded on geometric domains or on the nodes of weighted networks. Datasets comprising such objects arise in many domains of scientific and practical interest. For example, could represent a functional magnetic resonance image, or the nodes of a social network labeled with attributes or preferences, where the underlying metric structure is given by the shortest path distance, commute distance, or diffusion distance. Formally, these may be viewed as functions defined on metric spaces, sometimes equipped with additional structure such as a probability measure, in which case the domain is referred to as a metric-measure space, or simply -space. Our primary goal is threefold: (i) to develop metrics that allow us to model and quantify variation in functional data, possibly…
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Taxonomy
TopicsTopological and Geometric Data Analysis
