Salt Lake City, UT
Oct. 8, 2000 to Oct. 13, 2000
Robert R. Johnson , University of Utah
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.
Multidimensional Visualization, Vector Data Fusion, Multidimensional Geometry
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