  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.
