Fréchet Means for Distributions of Persistence diagrams
Katharine Turner, Yuriy Mileyko, Sayan Mukherjee, John Harer · arXiv (Cornell University) · 2012
Given a distribution $ρ$ on persistence diagrams and observations $X_1,...X_n \stackrel{iid}{\sim} ρ$ we introduce an algorithm in this paper that estimates a Fréchet mean from the set of diagrams $X_1,...X_n$. If the underlying measure $ρ$ is a combination of Dirac masses $ρ= \frac{1}{m} \sum_{i=1}^m δ_{Z_i}$ then we prove the algorithm converges to a local minimum and a law of large numbers result for a Fréchet mean computed by the algorithm given observations drawn iid from $ρ$. We illustrate the convergence of an empirical mean computed by the algorithm to a population mean by simulations from Gaussian random fields.