Entropy 2011, 13(9), 1694-1707; https://doi.org/10.3390/e13091694
Tsallis Mutual Information for Document Classification
Institut d’Informàtica i Aplicacions, Universitat de Girona, Campus Montilvi, Girona 17071, Spain
*
Author to whom correspondence should be addressed.
Received: 1 August 2011 / Revised: 5 September 2011 / Accepted: 8 September 2011 / Published: 14 September 2011
(This article belongs to the Special Issue Tsallis Entropy)
Abstract
Mutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback–Leibler distance, the difference between entropy and conditional entropy, and the Jensen–Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration. View Full-Text
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
Share & Cite This Article
MDPI and ACS Style
Vila, M.; Bardera, A.; Feixas, M.; Sbert, M. Tsallis Mutual Information for Document Classification. Entropy 2011, 13, 1694-1707.