Estimation of entropic measures of association

As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multi-valued relations. This distinction is especially important when high fidelity models do not exist and where the sensed phenomena is projected into a measurement space. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented and tested against a data set used in a standard data mining competition that features both sparse categorical and continuous valued descriptors of a target. The quantitative and computational results support the conclusion that the proposed methodology is promising for general purpose low level data fusion.