Geodesic Regression on Riemannian Manifolds

@inproceedings{Fletcher2011GeodesicRO,
  title={Geodesic Regression on Riemannian Manifolds},
  author={P. Thomas Fletcher},
  year={2011}
}
This paper introduces a regression method for modeling the relationship between a manifold-valued random variable and a real-valued independent parameter. The principle is to fit a geodesic curve, parameterized by the independent parameter, that best fits the data. Error in the model is evaluated as the sum-of-squared geodesic distances from the model to the data, and this provides an intrinsic least squares criterion. Geodesic regression is, in some sense, the simplest parametric model that… CONTINUE READING
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Size and shape spaces for landmark data in two dimensions (with discussion)

  • F. L. Bookstein
  • Statistical Science 1(2), 181–242
  • 1986
Highly Influential
5 Excerpts

A second-order model for time-dependent data interpolation: Splines on shape spaces

  • A. Trouvé, F. X. Vialard
  • MICCAI STIA Workshop
  • 2010
2 Excerpts

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