Nearest Neighbor Distributions and Noise Variance Estimation

In this paper, we address the problem of deriving bounds for the moments of nearest neighbor distributions. The bounds are formulated for the general case and specifically applied to the problem of noise variance estimation with the Delta test and the Gamma test. For this problem, we focus on the rate of convergence and the bias of the estimators and validate the theoretical achievements with experimental results.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Elia Liitiäinen, Francesco Corona, Amaury Lendasse
• Keywords: Delta Test, ESANN 2007, Nearest Neighbor Distributions, Neural Networks, Noise Variance Estimation