Persistence Landscape
swMATH ID:  21260 
Software Authors:  Bubenik, Peter; Dłotko, Paweł 
Description:  A persistence landscapes toolbox for topological statistics. Topological data analysis provides a multiscale description of the geometry and topology of quantitative data. The persistence landscape is a topological summary that can be easily combined with tools from statistics and machine learning. We give efficient algorithms for calculating persistence landscapes, their averages, and distances between such averages. We discuss an implementation of these algorithms and some related procedures. These are intended to facilitate the combination of statistics and machine learning with topological data analysis. We present an experiment showing that the lowdimensional persistence landscapes of points sampled from spheres (and boxes) of varying dimensions differ. 
Homepage:  https://www.math.upenn.edu/%7Edlotko/persistenceLandscape.html 
Keywords:  topological data analysis; persistent homology; statistical topology; topological machine learning; intrinsic dimension 
Related Software:  TDA; Ripser; PersistenceImages; GitHub; Gudhi; PHAT; Perseus; Dionysus; javaPlex; Flagser; GIComplex; Thrust; SimpPers; RIVET; SimBa; DIPHA; Gprof; Julia; Eirene; factoextra 
Referenced in:  18 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

A persistence landscapes toolbox for topological statistics. Zbl 1348.68186 Bubenik, Peter; Dłotko, Paweł 
2017

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Referenced by 36 Authors
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Referenced in 10 Serials
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