Rubio-Sánchez, ManuelSanchez, AlbertoLehmann, Dirk J.Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke2017-06-122017-06-1220171467-8659https://doi.org/10.1111/cgf.13196https://diglib.eg.org:443/handle/10.1111/cgf13196Radial axes plots are multivariate visualization techniques that extend scatterplots in order to represent high-dimensional data as points on an observable display. Well-known methods include star coordinates or principal component biplots, which represent data attributes as vectors that de ne axes, and produce linear dimensionality reduction mappings. In this paper we propose a hybrid approach that bridges the gap between star coordinates and principal component biplots, which we denominate adaptable radial axes plots . It is based on solving convex optimization problems where users can: (a) update the axis vectors interactively, as in star coordinates, while producing mappings that enable to estimate attribute values optimally through labeled axes, similarly to principal component biplots; (b) use different norms in order to explore additional nonlinear mappings of the data; and (c) include weights and constraints in the optimization problems for sorting the data along one axis. The result is a exible technique that complements, extends, and enhances current radial methods for data analysis.[Humancentered computing]VisualizationVisualization techniques [Probability and statistics]Statistical paradigmsStatistical graphics [Humancentered computing]VisualizationVisualization theoryconcepts and paradigms [Probability and statistics]Statistical paradigmsExploratory data analysis10.1111/cgf.13196389-399