MEASURES OF DISSIMILARITIES FOR CONTRASTING INFORMATION-SOURCES IN DATA FUSION

Information content of data coming from a given source is modelled and formalized as a probability distribution and, as such, considered as a point in a function space, where a concept of distance can be introduced. In such a space, Kullback-Leibler Information is a contrast function able to measure dissimilarities between probability distributions and, then, a practical index for clustering different information sources according to the quality of their content.