Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Mithun Das Gupta, Thomas Huang
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.
PDF Link: /papers/10/p220-das_gupta.pdf
AUTHOR = "Mithun Das Gupta
and Thomas Huang",
TITLE = "Regularized Maximum Likelihood for Intrinsic Dimension Estimation",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "220--227"