%0 Journal Article
%T Regularized Maximum Likelihood for Intrinsic Dimension Estimation
%A Mithun Das Gupta
%A Thomas S. Huang
%J Computer Science
%D 2012
%I arXiv
%X 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.
%U http://arxiv.org/abs/1203.3483v1