Discrete & Computational Geometry

, Volume 39, Issue 1–3, pp 419–441

Finding the Homology of Submanifolds with High Confidence from Random Samples

Article

Abstract

Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high-dimensional spaces. We consider the case where data are drawn from sampling a probability distribution that has support on or near a submanifold of Euclidean space. We show how to “learn” the homology of the submanifold with high confidence. We discuss an algorithm to do this and provide learning-theoretic complexity bounds. Our bounds are obtained in terms of a condition number that limits the curvature and nearness to self-intersection of the submanifold. We are also able to treat the situation where the data are “noisy” and lie near rather than on the submanifold in question.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Partha Niyogi
    • 1
  • Stephen Smale
    • 2
  • Shmuel Weinberger
    • 3
  1. 1.Departments of Computer Science and StatisticsUniversity of ChicagoChicagoUSA
  2. 2.Toyota Technological InstituteChicagoUSA
  3. 3.Department of MathematicsUniversity of ChicagoChicagoUSA

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