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Intrinsic Dimensionality Estimation of Submanifolds in Euclidean space

2005

Conference Paper

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We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean space from random samples. The method is based on the convergence rates of a certain U-statistic on the manifold. We solve at least partially the question of the choice of the scale of the data. Moreover the proposed method is easy to implement, can handle large data sets and performs very well even for small sample sizes. We compare the proposed method to two standard estimators on several artificial as well as real data sets.

Author(s): Hein, M. and Audibert, Y.
Journal: Proceedings of the 22nd International Conference on Machine Learning
Pages: 289
Year: 2005
Day: 0
Editors: De Raedt, L. , S. Wrobel

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICML Bonn

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{3470,
  title = {Intrinsic Dimensionality Estimation of Submanifolds in Euclidean space},
  author = {Hein, M. and Audibert, Y.},
  journal = {Proceedings of the 22nd International Conference on Machine Learning},
  pages = {289 },
  editors = {De Raedt, L. , S. Wrobel},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005}
}