The persistence landscape and some of its properties

Abstract

Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine learning. Furthermore, the mapping from persistence diagrams to persistence landscapes is stable and invertible. We introduce a weighted version of the persistence landscape and define a one-parameter family of Poisson-weighted persistence landscape kernels that may be useful for learning. We also demonstrate some additional properties of the persistence landscape. First, the persistence landscape may be viewed as a tropical rational function. Second, in many cases it is possible to exactly reconstruct all of the component persistence diagrams from an average persistence landscape. It follows that the persistence landscape kernel is characteristic for certain generic empirical measures. Finally, the persistence landscape distance may be arbitrarily small compared to the interleaving distance.

18 pages, to appear in the Proceedings of the 2018 Abel Symposium

References in corpus (5)

• Sliced Wasserstein Kernel for Persistence Diagrams
• Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals
• Graded persistence diagrams and persistence landscapes
• Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia
• Topological Data Analysis of Clostridioides difficile Infection and Fecal Microbiota Transplantation

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