  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.
