Calibrate Your Eyes to Recognize High-Dimensional Shapes from Their Low-Dimensional Projections

Dianne Cook

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This paper provides a suite of datasets from standard multivariate distributions and simple high-dimensional geomtric shapes that can be used to visually calibrate new users of grand tours. It contains animations of 1-D, 2-D, 3-D, 4-D and 5-D grand tours, links to starting XGobi or XLispStat on the calibration data sets, and C code for generating a grand tour.

The purpose of the paper is two-fold: providing code for the grand tour that others could pick up and modify (it is not easy to code this version which is why there are very few implementations currently available), and secondly, provide a variety of training datasets to help new users get a visual sense for high-dimensional data.

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