Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

Ruizhen Hu, Bin Chen, Juzhan Xu, Oliver van Kaick, Oliver Deussen, Hui Huang

View presentation:2021-10-29T13:30:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Ordering axes for a set of star glyphs using reinforcement learning. For the same set of high-dimensional data points, we can optimize the axis order of the corresponding star glyphs according to different perceptual criteria: (up) spike and salient shape strategies that place dissimilar axes close-by; (down) maximizing class separability. The class labels of the glyphs are indicated by blue/red color. Star glyphs of the same data point (but with different axis orders) are drawn at the same position inside each plot for comparison.
Fast forward

Direct link to video on YouTube:


Star glyph set, coordinate ordering, reinforcement learning, shape context


We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set. To find the optimal coordinate order for the given set, we train a neural network using reinforcement learning to reward orderings with high silhouette coefficients. The network consists of an encoder and a decoder with an attention mechanism. The encoder employs a recurrent neural network (RNN) to encode input shape and class information, while the decoder together with the attention mechanism employs another RNN to output a sequence with the new coordinate order. In addition, we introduce a neural network to efficiently estimate the similarity between shape context descriptors, which allows to speed up the computation of silhouette coefficients and thus the training of the axis ordering network. Two user studies demonstrate that the orders provided by our method are preferred by users for perceiving class separation. We tested our model on different settings to show its robustness and generalization abilities and demonstrate that it allows to order input sets with unseen data size, data dimension, or number of classes. We also demonstrate that our model can be adapted to coordinate ordering of other types of plots such as RadViz by replacing the proposed shape-aware silhouette coefficient with the corresponding quality metric to guide network training.