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Proceedings Paper

Hypercube processing of mixed sensed data entropic associations
Author(s): Paul Deignan; Antone Kusmanoff
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Paper Abstract

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.

Paper Details

Date Published: 16 May 2011
PDF: 14 pages
Proc. SPIE 8053, Geospatial InfoFusion Systems and Solutions for Defense and Security Applications, 80530J (16 May 2011); doi: 10.1117/12.884477
Show Author Affiliations
Paul Deignan, L-3 Communications (United States)
Antone Kusmanoff, L-3 Communications (United States)


Published in SPIE Proceedings Vol. 8053:
Geospatial InfoFusion Systems and Solutions for Defense and Security Applications
Matthew F. Pellechia; Richard Sorensen; Shiloh L. Dockstader; Rudy G. Benz II; Bernard V. Brower, Editor(s)

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