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Sankhya A

pp 1–42 | Cite as

Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes

  • Valentina Masarotto
  • Victor M. PanaretosEmail author
  • Yoav Zemel
Article

Abstract

Covariance operators are fundamental in functional data analysis, providing the canonical means to analyse functional variation via the celebrated Karhunen–Loève expansion. These operators may themselves be subject to variation, for instance in contexts where multiple functional populations are to be compared. Statistical techniques to analyse such variation are intimately linked with the choice of metric on covariance operators, and the intrinsic infinite-dimensionality of these operators. In this paper, we describe the manifold-like geometry of the space of trace-class infinite-dimensional covariance operators and associated key statistical properties, under the recently proposed infinite-dimensional version of the Procrustes metric (Pigoli et al. Biometrika 101, 409–422, 2014). We identify this space with that of centred Gaussian processes equipped with the Wasserstein metric of optimal transportation. The identification allows us to provide a detailed description of those aspects of this manifold-like geometry that are important in terms of statistical inference; to establish key properties of the Fréchet mean of a random sample of covariances; and to define generative models that are canonical for such metrics and link with the problem of registration of warped functional data.

Keywords and phrases.

Functional data analysis Fréchet mean Manifold statistics Optimal coupling Tangent space PCA Trace-class operator. 

AMS (2000) subject classification.

Primary 60G15 Gaussian processes 60D05 Geometric probability and stochastic geometry Secondary 60H25 Random operators and equations 62M99 None of the above but in this section. 

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Notes

Acknowledgements

We wish to warmly thank a reviewer for providing constructive and insightful comments that led to genuine improvements in our presentation. This research is supported in part by a Swiss National Science Foundation grant to V. M. Panaretos.

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Copyright information

© Indian Statistical Institute 2018

Authors and Affiliations

  • Valentina Masarotto
    • 1
  • Victor M. Panaretos
    • 1
    Email author
  • Yoav Zemel
    • 1
  1. 1.Institut de MathématiquesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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