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Geometric Metrics for Topological Representations

In: Handbook of Variational Methods for Nonlinear Geometric Data

Author

Listed:
  • Anirudh Som

    (School of Arts, Media and Engineering, Arizona State University, School of Electrical, Computer and Energy Engineering)

  • Karthikeyan Natesan Ramamurthy

    (IBM Research)

  • Pavan Turaga

    (School of Arts, Media and Engineering, Arizona State University, School of Electrical, Computer and Energy Engineering)

Abstract

In this chapter, we present an overview of recent techniques from the emerging area of topological data analysis (TDA), with a focus on machine-learning applications. TDA methods are concerned with measuring shape-related properties of point-clouds and functions, in a manner that is invariant to topological transformations. With a careful design of topological descriptors, these methods can result in a variety of limited, yet practically useful, invariant representations. The generality of this approach results in a flexible design choice for practitioners interested in developing invariant representations from diverse data sources such as image, shapes, and time-series data. We present a survey of topological representations and metrics on those representations, discuss their relative pros and cons, and illustrate their impact on a few application areas of recent interest.

Suggested Citation

  • Anirudh Som & Karthikeyan Natesan Ramamurthy & Pavan Turaga, 2020. "Geometric Metrics for Topological Representations," Springer Books, in: Philipp Grohs & Martin Holler & Andreas Weinmann (ed.), Handbook of Variational Methods for Nonlinear Geometric Data, chapter 0, pages 415-441, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-31351-7_15
    DOI: 10.1007/978-3-030-31351-7_15
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