Persistent Cross Entropy

Abstract:Persistent entropy is the Shannon entropy of a persistence-based probability measure defined on a persistence diagram. However, its cross-entropy version is not naturally defined because two persistence diagrams generally have different event spaces. To bridge these event spaces, we combine a similarity function with persistence weighting to define an induced probability. The induced probability reflects information from one diagram on the event space of the other diagram and assigns unexplained probability mass to the unexplained event. Using the induced probability, we extend cross entropy to persistence diagrams, called persistent cross entropy (PCE). We establish the main properties of both the induced probability and PCE and prove stability theorems for both. Through three numerical studies, we show that PCE distinguishes diagrams with the same persistent entropy, separates causal directions in dynamical systems without constructing a joint persistent diagram, and can be used as a directional topology loss for knowledge distillation.

Machine Learning (cs.LG); Information Theory (cs.IT)

arXiv:2608.24549 [cs.LG]

(or arXiv:2608.24549v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.24549 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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