17 May 2011 Hypercube processing of mixed sensed data entropic associations
Author Affiliations +
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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 (17 May 2011); doi: 10.1117/12.884477; https://doi.org/10.1117/12.884477
PROCEEDINGS
14 PAGES


SHARE
KEYWORDS
Data fusion

Sensors

Target recognition

Data modeling

Data mining

Target detection

Detection and tracking algorithms

RELATED CONTENT

Adaptive context exploitation
Proceedings of SPIE (May 28 2013)
A fusion approach for coarse-to-fine target recognition
Proceedings of SPIE (April 18 2006)
A Bayesian network tracking database
Proceedings of SPIE (August 25 2004)
An approach to target detection in forested scenes
Proceedings of SPIE (April 16 2008)

Back to Top