Abstract

Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. In this paper we discuss 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. 1 Introduction This paper builds on dynamic visualization methods for high-dimensional data using low-dimensional projections. Among these methods, the most familiar are 3-D data rotations, generated by displaying a continuous sequence of 2-D projections of 3-D data. From a statistical perspective it is rare to have data that are strictly 3-D, and so, unlike most computer graphics applications, the more useful methods for data analysis show projections from a...

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