Proceedings Article | 29 May 2013
Proc. SPIE. 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
KEYWORDS: Information fusion, Visual analytics, Visualization, Electroluminescence, Solids, Finite element methods, Feature selection, Brain-machine interfaces, Neodymium, Optimization (mathematics)
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.