We demonstrate applications of the Gaussian process-based landmarking algorithm proposed in [T. Gao, S. Z. Kovalsky, and I. Daubechies, SIAM J. Math. Data Sci., 1 (2019), pp. 208--236] to geometric morphometrics, a branch of evolutionary biology centered at the analysis and comparisons of anatomical shapes, and compare the automatically sampled landmarks with the “ground truth” landmarks manually placed by evolutionary anthropologists; the results suggest that Gaussian process landmarks perform equally well or better, in terms of both spatial coverage and downstream statistical analysis. We provide a detailed exposition of numerical procedures and feature filtering algorithms for computing high-quality and semantically meaningful diffeomorphisms between disk-type anatomical surfaces.


  1. Gaussian process
  2. experimental design
  3. active learning
  4. manifold learning
  5. geometric morphometrics

MSC codes

  1. 60G15
  2. 62K05
  3. 65D18

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Supplementary Material

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Index of Supplementary Materials

Title of paper: Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics

Authors: Tingran Gao, Shahar Kovalsky, Doug Boyer, and Ingrid Daubechies

File: supplement.pdf

Type: PDF

Contents: Additional figures, proof of a theorem in the main text, and comparative biological discussions.


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Information & Authors


Published In

cover image SIAM Journal on Mathematics of Data Science
SIAM Journal on Mathematics of Data Science
Pages: 237 - 267
ISSN (online): 2577-0187


Submitted: 27 July 2018
Accepted: 27 December 2018
Published online: 12 February 2019


  1. Gaussian process
  2. experimental design
  3. active learning
  4. manifold learning
  5. geometric morphometrics

MSC codes

  1. 60G15
  2. 62K05
  3. 65D18



Funding Information

National Science Foundation https://doi.org/10.13039/100000001 : BCS-1552848

Funding Information

Simons Foundation https://doi.org/10.13039/100000893 : 400837

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