ShaRP: Shape-Regularized Multidimensional Projections
This work addresses the need for customizable visualizations in interactive data analysis, offering an incremental improvement over existing projection methods.
The authors tackled the problem of controlling the visual signature in multidimensional projections for high-dimensional data exploration, resulting in ShaRP, a technique that allows explicit user control over scatterplot shapes with minimal quality trade-offs.
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.