Model selection for simplicial approximation Caillerie, Claire; Michel, Bertrand

User activity

Share to:
View the summary of this work
Caillerie, Claire ; Michel, Bertrand
Appears In
Foundations of Computational Mathematics
computational geometry; geometrical inference; slope heuristics
In the computational geometry field, simplicial complexes have been used to describe an underlying geometric shape knowing a point cloud sampled on it. In this article, an adequate statistical framework is first proposed for the choice of a simplicial complex among a parametrized family. A least-squares penalized criterion is introduced to choose a complex, and a model selection theorem states how to select the "best" model, from a statistical point of view. This result gives the shape of the penalty, and then the "slope heuristics method" is used to calibrate the penalty from the data. Some experimental studies on simulated and real datasets illustrate the method for the selection of graphs and simplicial complexes of dimension two. Keywords Computational geometry * Geometric inference * Simplicial complexes * Model selection * Penalization * Slope heuristics * Spectral clustering Mathematics Subject Classification (2000) 62H12 * 62H30 * 05E45 * 55U10
Work ID

User activity

e.g. test cricket, Perth (WA), "Parkes, Henry"

Separate different tags with a comma. To include a comma in your tag, surround the tag with double quotes.

Be the first to add a tag for this work

Be the first to add this to a list

Comments and reviews

What are comments? Add a comment

No user comments or reviews for this work

Add a comment

Show comments and reviews from Amazon users