@inproceedings{3f18bb6ca70746af98da399ecdb439d5,
title = "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.",
author = "Gupta, {Mithun Das} and Huang, {Thomas S.}",
year = "2010",
language = "English (US)",
isbn = "9780974903965",
series = "Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010",
publisher = "AUAI Press",
pages = "220--227",
booktitle = "Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010",
}