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 d...
This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data pattern...
Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II...
— Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been...
In this paper, supervised nonparametric information theoretic classification (ITC) is introduced. Its principle relies on the likelihood of a data sample of transmitting its class...
Abstract. Cluster validation to determine the right number of clusters is an important issue in clustering processes. In this work, a strategy to address the problem of cluster val...