Wasserstein Stability of Persistence Landscapes and Barcodes

Abstract: Barcodes form a complete set of invariants for interval decomposable persistence modules and are an important summary in topological data analysis. The set of barcodes is equipped with a canonical one-parameter family of metrics, the $p-$Wasserstein distances. However, the $p-$Wasserstein distances depend on a choice of a metric on the set of interval modules, and there is no canonical choice. One convention is to use the length of the symmetric difference between 2 intervals, which equals to the 1-norm of the difference between their Hilbert functions. We propose a new metric for interval modules based on the rank invariant instead of the dimension invariant. Our metric is topologically equivalent to the metrics induced by the $p-$norms on $\R^2$. We establish stability results from filtered CW complexes to barcodes, as well as from barcodes to persistence landscapes. In particular, we show that vectorization via persistence landscapes is 1-Lipschitz with a sharp bound, with respect to the 1-Wasserstein distance on barcodes and the 1-norm on persistence landscapes.