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Proceedings Paper

Spatial voting for automatic feature selection, fusion, and visualization
Author(s): Holger Jaenisch; James Handley
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Paper Abstract

We present a novel feature selection, fusion, and visualization utility using Spatial Voting (SV). This SV feature optimization utility is designed to be an off-line stand-alone utility to help an investigator find useful feature pairs for cluster analysis and lineage identification. The analysis can be used to enable the analyst to vary parameters manually and explore the best combination that yields visually appealing or significant groups or spreading of data points depending on the planned use of the analysis downstream. Several different criteria are available to the user in order to determine the best SV grid size and feature pair including minimizing zeros, minimizing covariance, balanced minimum covariance, or the maximization of one of eight different scoring metrics: Containment, Rand Index, Purity, Precision, Recall, F-Score, Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). The tool that is described in this work facilitates this analysis and makes it simple, efficient, and interactive if the analyst so desires.

Paper Details

Date Published: 29 May 2013
PDF: 23 pages
Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 87560B (29 May 2013); doi: 10.1117/12.2012449
Show Author Affiliations
Holger Jaenisch, Licht Strahl Engineering, Inc. (United States)
Johns Hopkins Univ. (United States)
Alabama A&M Univ. (United States)
James Handley, Licht Strahl Engineering, Inc. (United States)


Published in SPIE Proceedings Vol. 8756:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
Jerome J. Braun, Editor(s)

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