ShaRP: Shape-Regularized Multidimensional Projections
Alister Machado Alexandru Telea Michael Behrisch
Projections, or dimensionality reduction methods, are techniques of choice
for the visual exploration of high-dimensional data. Many such techniques
exist, each one of them having a distinct visual signature - i.e., a
recognizable way to arrange points in the resulting scatterplot. Such
signatures are implicit consequences of algorithm design, such as whether the
method focuses on local vs global data pattern preservation; optimization
techniques; and hyperparameter settings. We present a novel projection
technique - ShaRP - that provides users explicit control over the visual
signature of the created scatterplot, which can cater better to interactive
visualization scenarios. ShaRP scales well with dimensionality and dataset
size, generically handles any quantitative dataset, and provides this extended
functionality of controlling projection shapes at a small, user-controllable
cost in terms of quality metrics.