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Proceedings Paper

Evolutionary data visualization
Author(s): Steven C. Gustafson; Andrew W. Learn; Gordon R. Little; John S. Loomis
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Paper Abstract

Given data points that sample an unknown function in one independent variable, techniques are described and illustrated that generate additional data points `similar to' the given points. These techniques are optimal in two key respects. First, each technique models the data using a continuous family of functions, where each function is the smoothest possible in that energy is minimized. Here energy is a linear combination of lack-of-smoothness (defined as integrated squared second derivative of the function) and lack-of-fit (defined as sum squared deviation of the function from either the given points or the given points displaced to intersect their least squares line). Second, many members of the family compete in a robust evolutionary process to acquire energy, and the result of this competition determines the relative contribution of each member function. The techniques model the given points in that they yield probability density functions of the dependent variable for any value of the independent variable. Thus they enable the implementation of many pattern recognition and data visualization procedures.

Paper Details

Date Published: 23 July 1999
PDF: 10 pages
Proc. SPIE 3694, Modeling, Simulation, and Visualization for Real and Virtual Environments, (23 July 1999); doi: 10.1117/12.354467
Show Author Affiliations
Steven C. Gustafson, Air Force Institute of Technology (United States)
Andrew W. Learn, Air Force Institute of Technology (United States)
Gordon R. Little, Univ. of Dayton (United States)
John S. Loomis, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 3694:
Modeling, Simulation, and Visualization for Real and Virtual Environments
Nickolas L. Faust; Steve Kessinger; Steve Kessinger, Editor(s)

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