Regularized Maximum Likelihood for Intrinsic Dimension Estimation

Details Regularized Maximum Likelihood for Intrinsic Dimension Estimation Regularized Maximum Likelihood for Intrinsic Dimension Estimation

2012-03-15

arxiv.org/abs/1203.3483v1

Computer Science

Learning

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

Scientific paper

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.

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