Reconstruction of univariate functions from directional persistence diagrams

03/03/2022

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by   Aina Ferrà, et al.

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We describe a method for approximating a single-variable function f using persistence diagrams of sublevel sets of f from height functions in different directions. We provide algorithms for the piecewise linear case and for the smooth case. Three directions suffice to locate all local maxima and minima of a piecewise linear continuous function from its collection of directional persistence diagrams, while five directions are needed in the case of smooth functions with non-degenerate critical points. Our approximation of functions by means of persistence diagrams is motivated by a study of importance attribution in machine learning, where one seeks to reduce the number of critical points of signal functions without a significant loss of information for a neural network classifier.