Uncertainty in Artificial Intelligence
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Regularized Maximum Likelihood for Intrinsic Dimension Estimation
Mithun Das Gupta, Thomas Huang
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.
Keywords:
Pages: 220-227
PS Link:
PDF Link: /papers/10/p220-das_gupta.pdf
BibTex:
@INPROCEEDINGS{Das Gupta10,
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"
}


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