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Adaptable Radial Axes Plots for Improved Multivariate Data Visualization
Author(s) -
RubioSánchez M.,
Sanchez A.,
Lehmann D. J.
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13196
Subject(s) - principal component analysis , biplot , parallel coordinates , visualization , dimensionality reduction , principal axis theorem , computer science , multivariate statistics , sorting , curse of dimensionality , dimension (graph theory) , rank (graph theory) , data visualization , mathematics , algorithm , data mining , artificial intelligence , geometry , biochemistry , chemistry , combinatorics , machine learning , pure mathematics , genotype , gene
Radial 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 define 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 flexible technique that complements, extends, and enhances current radial methods for data analysis.