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.
Recommended Citation
Caţaron, A., Andonie, R., Chueh, Y. (2013). Asymptotically Unbiased Estimator of the Informational Energy with kNN. International Journal of Computers Communications & Control, 8(5), 689-698.
Journal
International Journal of Computers Communications & Control
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Rights
Copyright © 2006-2013 by CCC Publications
Comments
This article was originally published Open Access in International Journal of Computers Communications & Control. The full-text article from the publisher can be found here.