ShaRP: Shape-Regularized Multidimensional Projections

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

Scaling Up the Explanation of Multidimensional Projections

We present a set of interactive visual analysis techniques aiming at explaining data patterns in multidimensional projections. Our novel techniques include a global value-based encoding that highlights point groups having outlier values in any dimension as well as several local tools that provide details on the statistics of all dimensions for a user-selected projection area. Our techniques generically apply to any projection algorithm and scale computationally well to hundreds of thousands of points and hundreds of dimensions. We describe a user study that shows that our visual tools can be quickly learned and applied by users to obtain non-trivial insights in real-world multidimensional datasets

Visual Exploration of Neural Network Projection Stability

We present a method to visually assess the stability of deep learned projections. For this, we perturb the high-dimensional data by controlled sequences and visualize the resulting changes in the 2D projection. We apply our method to a recent deep learned projection framework on several training configurations (learned projections and real-world datasets). Our method, which is simple to implement, runs at interactive rates, sheds several novel insights on the stability of the explored method

Identifying Cluttering Edges in Near-Planar Graphs

Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creating readable drawings of nearly planar graphs remains a challenge. We therefore seek to answer which edges of nearly planar graphs create clutter in their drawings generated by mainstream graph drawing algorithms. We present a heuristic to identify problematic edges in nearly planar graphs and adjust their weights in order to produce higher quality layouts with spring-based drawing algorithms. Our experiments show that our heuristic produces significantly higher quality drawings for augmented grid graphs, augmented triangulations, and deep triangulations