Topological data analysis using persistent discrete homology

We propose persistent discrete homology as a tool for topological data analysis and discuss its advantages over the existing methods.

In particular, we provide empirical evidence that persistent discrete homology is more noise-resistant than persistent homology of the Vietoris-Rips complex for data coming from non-metric settings.