Maximum Likelihood Estimation of Intrinsic Dimension
Abstract
We propose a new method for estimating intrinsic dimension of a dataset derived by applying the principle of maximum likelihood to the distances between close neighbors. We derive the estimator by a Poisson process approximation, assess its bias and variance theo- retically and by simulations, 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.
Cite
Text
Levina and Bickel. "Maximum Likelihood Estimation of Intrinsic Dimension." Neural Information Processing Systems, 2004.Markdown
[Levina and Bickel. "Maximum Likelihood Estimation of Intrinsic Dimension." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/levina2004neurips-maximum/)BibTeX
@inproceedings{levina2004neurips-maximum,
title = {{Maximum Likelihood Estimation of Intrinsic Dimension}},
author = {Levina, Elizaveta and Bickel, Peter J.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {777-784},
url = {https://mlanthology.org/neurips/2004/levina2004neurips-maximum/}
}