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International Conference on Geometric Science of Information

GSI 2019: Geometric Science of Information pp 343–350Cite as

Topological Methods for Unsupervised Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11712))

Abstract

Unsupervised learning is a broad topic in machine learning with many diverse sub-disciplines. Within the field of unsupervised learning we will consider three major topics: dimension reduction; clustering; and anomaly detection. We seek to use the languages of topology and category theory to provide a unified mathematical approach to these three major problems in unsupervised learning.

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Author information

Authors and Affiliations

  1. Tutte Institute for Mathematics and Computing, Ottawa, Canada

    Leland McInnes

Authors
  1. Leland McInnes
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Correspondence to Leland McInnes .

Editor information

Editors and Affiliations

  1. Sony Computer Science Laboratories, Inc., Tokyo, Japan

    Frank Nielsen

  2. Thales, Limours, France

    Frédéric Barbaresco

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McInnes, L. (2019). Topological Methods for Unsupervised Learning. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2019. Lecture Notes in Computer Science(), vol 11712. Springer, Cham. https://doi.org/10.1007/978-3-030-26980-7_35

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  • DOI: https://doi.org/10.1007/978-3-030-26980-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26979-1

  • Online ISBN: 978-3-030-26980-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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