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Scale and curvature effects in principal geodesic analysis

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  • Lazar, Drew
  • Lin, Lizhen

Abstract

There is growing interest in using the close connection between differential geometry and statistics to model smooth manifold-valued data. In particular, much work has been done recently to generalize principal component analysis (PCA), the method of dimension reduction in linear spaces, to Riemannian manifolds. One such generalization is known as principal geodesic analysis (PGA). This paper, in a novel fashion, obtains Taylor expansions in scaling parameters introduced in the domain of objective functions in PGA. It is shown this technique not only leads to better closed-form approximations of PGA but also reveals the effects that scale, curvature and the distribution of data have on solutions to PGA and on their differences to first-order tangent space approximations. This approach should be able to be applied not only to PGA but also to other generalizations of PCA and more generally to other intrinsic statistics on Riemannian manifolds.

Suggested Citation

  • Lazar, Drew & Lin, Lizhen, 2017. "Scale and curvature effects in principal geodesic analysis," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 64-82.
    • Handle: RePEc:eee:jmvana:v:153:y:2017:i:c:p:64-82
    DOI: 10.1016/j.jmva.2016.09.009
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    References listed on IDEAS

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    1. Hornik, Kurt & Feinerer, Ingo & Kober, Martin & Buchta, Christian, 2012. "Spherical k-Means Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i10).
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    3. D. Rancourt & L.‐P. Rivest & J. Asselin, 2000. "Using orientation statistics to investigate variations in human kinematics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 81-94.
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    Cited by:

    1. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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