Separating Topological Noise from Features Using Persistent Entropy

  title={Separating Topological Noise from Features Using Persistent Entropy},
  author={Nieves Atienza and Roc{\'i}o Gonz{\'a}lez-D{\'i}az and Matteo Rucco},
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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A density-based approach to feature detection in persistence diagrams for firn data
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Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams
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Persistent Homology: Hole Detection in LiDAR Point Clouds with Topological Data Analysis by Suen
  • Wun Ki
  • Environmental Science
  • 2021
Laser scanning has been widely used in various applications because of its efficiency and accuracy. However, a common problem found in laser scanning is that there are holes contained in the point


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An entropy-based persistence barcode
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Characterisation of the Idiotypic Immune Network Through Persistent Entropy
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