Topology Preserving Data Reduction for Computing Persistent Homology
Summary
A framework for reducing dataset size while preserving topological structure relevant to persistent homology computations.
Research Context
This publication reflects graduate research conducted at the University of Cincinnati College of Engineering and Applied Science. It is included here to document the technical foundation behind Convergent Analytics' work in industrial analytics, applied AI, high-performance computing, and topological data analysis.
This peer-reviewed conference publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.
The work investigates techniques for reducing data volume while preserving the topological characteristics required for accurate persistent homology computation. The approach improves computational efficiency without sacrificing analytical value.
The research demonstrated that carefully designed reduction methods can significantly lower processing costs, helping make topological analytics more practical for large-scale datasets.
Citation
Malott, N.O., Sens, A.M., Wilsey, P.A. Topology Preserving Data Reduction for Computing Persistent Homology. IEEE Big Data 2020. DOI: 10.1109/BigData50022.2020.9378216