The VC Dimension of Metric Balls Under Fréchet and Hausdorff Distances

Authors Anne Driemel, Jeff M. Phillips, Ioannis Psarros

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Anne Driemel
  • University of Bonn, Germany
Jeff M. Phillips
  • University of Utah, Salt Lake City, USA
Ioannis Psarros
  • National & Kapodistrian University of Athens, Greece


We thank Peyman Afshani for useful discussions on the topic of this paper. We also thank the organizers of the 2016 NII Shonan Meeting "Theory and Applications of Geometric Optimization" where this research was initiated.

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Anne Driemel, Jeff M. Phillips, and Ioannis Psarros. The VC Dimension of Metric Balls Under Fréchet and Hausdorff Distances. In 35th International Symposium on Computational Geometry (SoCG 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 129, pp. 28:1-28:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


The Vapnik-Chervonenkis dimension provides a notion of complexity for systems of sets. If the VC dimension is small, then knowing this can drastically simplify fundamental computational tasks such as classification, range counting, and density estimation through the use of sampling bounds. We analyze set systems where the ground set X is a set of polygonal curves in R^d and the sets {R} are metric balls defined by curve similarity metrics, such as the Fréchet distance and the Hausdorff distance, as well as their discrete counterparts. We derive upper and lower bounds on the VC dimension that imply useful sampling bounds in the setting that the number of curves is large, but the complexity of the individual curves is small. Our upper bounds are either near-quadratic or near-linear in the complexity of the curves that define the ranges and they are logarithmic in the complexity of the curves that define the ground set.

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomness, geometry and discrete structures
  • Theory of computation → Computational geometry
  • VC dimension
  • Fréchet distance
  • Hausdorff distance


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