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Manual Controls for High-Dimensional Data Projections

Abstract

Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. This article discusses how to manually control the variable contributions to the projection. The user has control of the way a particular variable contributes to the viewed projection and can interactively adjust the variable’s contribution. These manual controls complement the automatic views provided by a grand tour, or a guided tour, and give greatly improved flexibility to data analysts.

Type
Publication
In Journal of the Computational and Graphical Statistics
Date
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