Eric M. Hanson

Learn More
Many datasets can be viewed as a noisy sampling of an underlying topological space. Topo-logical data analysis aims to understand and exploit this underlying structure for the purpose of knowledge discovery. A fundamental tool of the discipline is persistent homology, which captures underlying data-driven, scale-dependent homological information. A(More)
1 Abstract Persistent homology is a relatively new tool from topo-logical data analysis that has transformed, for many, the way data sets (and the information contained in those sets) are viewed. It is derived directly from techniques in computational homology but has the added feature that it is able to capture structure at multiple scales. One way that(More)
  • 1