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16 May 2011Hypercube processing of mixed sensed data entropic associations
A method for calculating unbiased entropic estimates of multivariate associations between mixed data is given. Since
there is no assumption of unimodality of the distributions of the categorical and continuous-valued data, measures of
central dispersion are not appropriate for the quantification of association. Empirical estimates of entropic
associations are provided with respect to the partition entropy of a uniform binning interval and the cardinality of the
sensed data. The increased computational demand incurred by the appropriate generalized measure is mitigated by a
branch and bound algorithm for information-optimal attribute selection. The methodology is applied against a known
data set used in a standard data mining competition that features both sparse categorical and continuous valued
descriptors of a target with promising results.
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Paul Deignan, Antone Kusmanoff, "Hypercube processing of mixed sensed data entropic associations," Proc. SPIE 8053, Geospatial InfoFusion Systems and Solutions for Defense and Security Applications, 80530J (16 May 2011); https://doi.org/10.1117/12.884477