Separating Topological Noise from Features using Persistent Entropy

2016/05/10 by Atienza, Nieves, Gonzalez-Diaz, Rocio, Rucco, Matteo · 2 citations
#FOS: Computer and information sciences #Information Theory (cs.IT)

paper · doi:10.48550/arxiv.1605.02885

Abstract

In this paper, we derive a simple method for separating topological noise from topological features using a novel measure for comparing persistence barcodes called persistent entropy.

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• Efficient and faithful reconstruction of dynamical attractors using homogeneous differentiators
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