Title

Asymptotically Unbiased Estimator of the Informational Energy with kNN

Document Type

Article

Department or Administrative Unit

Mathematics

Publication Date

10-2013

Abstract

Motivated by machine learning applications (e.g., classification, function approximation, feature extraction), in previous work, we have introduced a non- parametric estimator of Onicescu’s informational energy. Our method was based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. In the present contribution, we discuss mathematical properties of this estimator. We show that our estimator is asymptotically unbiased and consistent. We provide further experimental results which illustrate the convergence of the estimator for standard distributions.

Journal

International Journal of Computers Communications & Control

Rights

Copyright © 2006-2013 by CCC Publications