Nonparametric Information Theoretic Clustering Algorithm
Abstract
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data are given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering that maximizes the estimated mutual information between data points and clusters. The improved performance of the proposed algorithm is demonstrated on several standard datasets. 1.
Cite
Text
Faivishevsky and Goldberger. "Nonparametric Information Theoretic Clustering Algorithm." International Conference on Machine Learning, 2010.Markdown
[Faivishevsky and Goldberger. "Nonparametric Information Theoretic Clustering Algorithm." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/faivishevsky2010icml-nonparametric/)BibTeX
@inproceedings{faivishevsky2010icml-nonparametric,
title = {{Nonparametric Information Theoretic Clustering Algorithm}},
author = {Faivishevsky, Lev and Goldberger, Jacob},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {351-358},
url = {https://mlanthology.org/icml/2010/faivishevsky2010icml-nonparametric/}
}