Topological Analysis of Scalar Fields with Outliers

Mickaël Buchet, Frédéric Chazal, Tamal K. Dey, Fengtao Fan, Steve Y. Oudot, Yusu Wang

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Given a real-valued function f defined over a manifold M embedded in Rd, we are interested in recovering structural information about f from the sole information of its values on a finite sample P. Existing methods provide approximation to the persistence diagram of f when geometric noise and functional noise are bounded. However, they fail in the presence of aberrant values, also called outliers, both in theory and practice. We propose a new algorithm that deals with outliers. We handle aberrant functional values with a method inspired from the k-nearest neighbors regression and the local median filtering, while the geometric outliers are handled using the distance to a measure. Combined with topological results on nested filtrations, our algorithm performs robust topological analysis of scalar fields in a wider range of noise models than handled by current methods. We provide theoretical guarantees and experimental results on the quality of our approximation of the sampled scalar field.

Original languageEnglish
Title of host publication31st International Symposium on Computational Geometry, SoCG 2015
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Number of pages15
ISBN (Electronic)9783939897835
Publication statusPublished - 1 Jun 2015
Externally publishedYes
Event31st International Symposium on Computational Geometry, SoCG 2015: SoCG 2015 - Eindhoven, Netherlands
Duration: 22 Jun 201525 Jun 2015


Conference31st International Symposium on Computational Geometry, SoCG 2015
Abbreviated titleSoCG 2015


  • Distance to a Measure
  • Nested Rips Filtration
  • Persistent Homology
  • Scalar Field Analysis
  • Topological Data Analysis

ASJC Scopus subject areas

  • Software

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