Regularized Maximum Likelihood for Intrinsic Dimension Estimation
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.
Cite
Text
Das Gupta and Huang. "Regularized Maximum Likelihood for Intrinsic Dimension Estimation." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Das Gupta and Huang. "Regularized Maximum Likelihood for Intrinsic Dimension Estimation." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/gupta2010uai-regularized/)BibTeX
@inproceedings{gupta2010uai-regularized,
title = {{Regularized Maximum Likelihood for Intrinsic Dimension Estimation}},
author = {Das Gupta, Mithun and Huang, Thomas S.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {220-227},
url = {https://mlanthology.org/uai/2010/gupta2010uai-regularized/}
}