On the stability of persistent entropy and new summary functions for TDA

Abstract

Persistent homology and persistent entropy have recently become useful tools for patter recognition. In this paper, we find requirements under which persistent entropy is stable to small perturbations in the input data and scale invariant. In addition, we describe two new stable summary functions combining persistent entropy and the Betti curve. Finally, we use the previously defined summary functions in a material classification task to show their usefulness in machine learning and pattern recognition.

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• Convergence of persistence diagrams for discrete time stationary processes
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• On the cosine similarity and orthogonality between persistence diagrams