Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル33rd International Conference on Machine Learning, ICML 2016
編集者Kilian Q. Weinberger, Maria Florina Balcan
出版社International Machine Learning Society (IMLS)
ページ2948-2957
ページ数10
ISBN(電子版)9781510829008
出版ステータスPublished - 2016
イベント33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
継続期間: 2016 6 192016 6 24

出版物シリーズ

名前33rd International Conference on Machine Learning, ICML 2016
4

Other

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

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • コンピュータ ネットワークおよび通信

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