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The grand tour is a method for viewing multivariate statistical data via orthogonal projections onto a sequence of two-dimensional subspaces. The sequence of subspaces is chosen so that it is dense in the set of all two-dimensional subspaces. Desirable properties of such sequences of subspaces are considered, and several specific types of sequences are tested for rapidity of becoming dense. Tabulations are provided of the minimum length of a grand tour sequence necessary to achieve various degrees of denseness in dimensions up to 20.
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Daniel Asimov (Tue,) studied this question.
synapsesocial.com/papers/6a1c284527b545b111a989f3 — DOI: https://doi.org/10.1137/0906011
Daniel Asimov
University of California, Davis
SIAM Journal on Scientific and Statistical Computing
University of California, Berkeley
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