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arXiv · 1203.3483

Regularized Maximum Likelihood for Intrinsic Dimension Estimation

Mithun Das Gupta·Thomas S. Huang
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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.

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Mithun Das Gupta, Thomas S. Huang. 2012-03-15. Regularized Maximum Likelihood for Intrinsic Dimension Estimation. https://arxiv.org/abs/1203.3483

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