Numeric Invariants from Multidimensional Persistence
Jacek Skryzalin, Gunnar Carlsson
TL;DR
This paper develops new numeric invariants derived from multidimensional persistence modules, enhancing the tools available for topological data analysis.
Contribution
It extends previous work by constructing novel numeric invariants from multidimensional persistence modules, building on earlier foundational results.
Findings
New numeric invariants from multidimensional persistence modules
Extension of previous theoretical frameworks
Potential applications in topological data analysis
Abstract
We extend the results of Adcock, Carlsson, and Carlsson by constructing numeric invariants from the computation of a multidimensional persistence module as given by Carlsson, Singh, and Zomorodian.
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