Adaptive Partitioning for Template Functions on Persistence Diagrams

  title={Adaptive Partitioning for Template Functions on Persistence Diagrams},
  author={Sarah Tymochko and Elizabeth Munch and Firas A. Khasawneh},
  journal={2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)},
As the field of Topological Data Analysis continues to show success in theory and in applications, there has been increasing interest in using tools from this field with methods for machine learning. Using persistent homology, specifically persistence diagrams, as inputs to machine learning techniques requires some mathematical creativity. The space of persistence diagrams does not have the desirable properties for machine learning, thus methods such as kernel methods and vectorization methods… Expand
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  • Peter Bubenik
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 2015
A new topological summary for data that is easy to combine with tools from statistics and machine learning and obeys a strong law of large numbers and a central limit theorem is defined. Expand