Regularized maximum likelihood for intrinsic dimension estimation

Mithun Das Gupta, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a new method for estimating the intrinsic dimension of a dataset by applying the principle of regularized maximum likelihood to the distances between close neighbors. We propose a regularization scheme which is motivated by divergence minimization principles. We derive the estimator by a Poisson process approximation, argue about its convergence properties and apply it to a number of simulated and real datasets. We also show it has the best overall performance compared with two other intrinsic dimension estimators.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Pages220-227
Number of pages8
StatePublished - 2010
Event26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 - Catalina Island, CA, United States
Duration: Jul 8 2010Jul 11 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Other

Other26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Country/TerritoryUnited States
CityCatalina Island, CA
Period7/8/107/11/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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