Hypercube processing of mixed sensed data entropic associations

Paul Deignan; Antone Kusmanoff

doi:10.1117/12.884477

16 May 2011 Hypercube processing of mixed sensed data entropic associations

Paul Deignan, Antone Kusmanoff

Proceedings Volume 8053, Geospatial InfoFusion Systems and Solutions for Defense and Security Applications; 80530J (2011) https://doi.org/10.1117/12.884477
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Paul Deignan and 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

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KEYWORDS

Data fusion

Sensors

Target recognition

Data modeling

Data mining

Target detection

Detection and tracking algorithms

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