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Towards Integrative Machine Learning and Knowledge Extraction pp 117–133Cite as

Persistent Topology for Natural Data Analysis — A Survey

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, liver and skin lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown.

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Acknowledgments

Article written within the activity of INdAM-GNSAGA.

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Authors and Affiliations

  1. Dip. di Matematica and ARCES, Univ. di Bologna, Bologna, Italy

    Massimo Ferri

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  1. Massimo Ferri
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Correspondence to Massimo Ferri .

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Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Ferri, M. (2017). Persistent Topology for Natural Data Analysis — A Survey. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_6

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