Fully Three-dimensional Radial Visualization
We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package $radviz3d$, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we use RadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) US regional SARS-Cov-2 variants' prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization
This paper presents 3D-RadViz, a visualization method for high dimensional data using a three dimensional radial visualization technique. The proposed technique extends the capabilities of the classical two dimensional r…
Data VisualizationDimension Reduction with Non-degrading Generalization
Visualizing high dimensional data by projecting them into two or three dimensional space is one of the most effective ways to intuitively understand the data's underlying characteristics, for example their class neighbor…
Data VisualizationDimensionality ReductionGeneral ClassificationRadial-VCReg: More Informative Representation Learning Through Radial Gaussianization
Self-supervised learning aims to learn maximally informative representations, but explicit information maximization is hindered by the curse of dimensionality. Existing methods like VCReg address this by regularizing fir…
Self-Supervised LearningRepresentation LearningGradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks w…
BIG-bench Machine LearningThree Dimensional Velocity Measurement Using a Dual Axis Millimeter-Wave Interferometric Radar
In this work, a method for directly measuring target velocity in three dimensions using a dual axis correlation interferometric radar is presented. Recent advances have shown that the measurement of a target's angular ve…