Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Machine Learning
2012-03-19 v1 Machine Learning
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
@article{arxiv.1203.3483,
title = {Regularized Maximum Likelihood for Intrinsic Dimension Estimation},
author = {Mithun Das Gupta and Thomas S. Huang},
journal= {arXiv preprint arXiv:1203.3483},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)