Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

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 allows a fast approximation technique. The method is applied into practical data on proteins and oxide glasses, and the results show the advantage of our method compared to other relevant methods on persistence diagrams.

Original languageEnglish
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsKilian Q. Weinberger, Maria Florina Balcan
PublisherInternational Machine Learning Society (IMLS)
Pages2948-2957
Number of pages10
ISBN (Electronic)9781510829008
Publication statusPublished - 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: 2016 Jun 192016 Jun 24

Publication series

Name33rd International Conference on Machine Learning, ICML 2016
Volume4

Other

Other33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period16/6/1916/6/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

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