Dimension Reduction of Two-Dimensional Persistence via Distance Deformations

03/01/2022

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by   Maximilian Neumann, et al.

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This article grew out of the application part of my Master's thesis at the Faculty of Mathematics and Information Science at Ruprecht-Karls-Universität Heidelberg under the supervision of PD Dr. Andreas Ott. In the context of time series analyses of RNA virus datasets with persistent homology, this article introduces a new method for reducing two-dimensional persistence to one-dimensional persistence by transforming time information into distances.