Maximum Likelihood Estimation of Intrinsic Dimension

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 theoretically 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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Elizaveta Levina, Peter J. Bickel
• Keywords: Intrinsic Dimension, Intrinsic Dimension Estimators, NIPS 2004, NIPS 2007, Poisson Process Approximation