A slice tour for finding hollowness in high-dimensional data
Abstract
Taking projections of high-dimensional data is a common analytical and visualisation technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualising data with concavities, or non-linear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualisation. This short technical note describes a simple approach for slicing in the orthogonal space of projections obtained when running a tour, thus presenting the viewer with an interpolated sequence of sliced projections. The method has been implemented in R as an extension to the tourr package, and can be used to explore for concave and non-linear structures in multivariate distributions.
Cite
@article{arxiv.1910.10854,
title = {A slice tour for finding hollowness in high-dimensional data},
author = {Ursula Laa and Dianne Cook and German Valencia},
journal= {arXiv preprint arXiv:1910.10854},
year = {2021}
}
Comments
13 pages, 6 figures