# Persistence weighted Gaussian kernel for topological data analysis

@inproceedings{Kusano2016PersistenceWG, title={Persistence weighted Gaussian kernel for topological data analysis}, author={Genki Kusano and Yasuaki Hiraoka and Kenji Fukumizu}, booktitle={ICML}, year={2016} }

Topological data analysis (TDA) is an emerging mathematical concept for characterizing shapes in complex data. In TDA, persistence diagrams are widely recognized as a useful descriptor of data, and can distinguish robust and noisy topological properties. This paper proposes a kernel method on persistence diagrams to develop a statistical framework in TDA. The proposed kernel satisfies the stability property and provides explicit control on the effect of persistence. Furthermore, the method…

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## 138 Citations

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