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Salt Lake City, UT
Oct. 8, 2000 to Oct. 13, 2000
ISBN: 0-7803-6478-3
pp: 50
Robert R. Johnson , University of Utah
ABSTRACT
Multi-dimensional entities are modeled, displayed, and understood with a new algorithm vectorizing data of any dimensionality. This algorithm is called SBP; it is a vectorized generalization of parallel coordinates. Classic geometries of any dimensionality can be demonstrated to facilitate perception and understanding of the shapes generated by this algorithm. SBP images of a 4D line, a circle, and 3D and 4D spherical helices are shown. A strategy for synthesizing multi-dimensional models matching multi-dimensional data is presented. Currrent applications include data mining; modeling data-defined structures of scientific interest such as protein structure and Calabi-Yau figures as multi-dimensional geometric entities; generating vector-fused data signature "finger prints" of classic frequency spectra that identify substances; and treating complex targets as multi-dimensional entities for automatic target recognition. SBP Vector Data Signatures apply to all pattern recognition problems.
INDEX TERMS
Multidimensional Visualization, Vector Data Fusion, Multidimensional Geometry
CITATION
Robert R. Johnson, "Visualization of Multi-Dimensional Data with Vector-fusion", IEEE_VIS, 2000, Visualization Conference, IEEE, Visualization Conference, IEEE 2000, pp. 50, doi:10.1109/VISUAL.2000.885708
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