Explaining three-dimensional dimensionality reduction plots

Understanding three-dimensional projections created by dimensionality reduction from high-variate datasets is very challenging. In particular, classical three-dimensional scatterplots used to display such projections do not explicitly show the relations between the projected points, the viewpoint us...

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Bibliographic Details
Published in:Information visualization 2016-04, Vol.15 (2), p.154-172
Main Authors: Coimbra, Danilo B, Martins, Rafael M, Neves, Tácito TAT, Telea, Alexandru C, Paulovich, Fernando V
Format: Article
Language:English
Subjects:
Data visualization
Datasets
Dimensionality reduction
explanatory visualization
Exploration
Information and software visualization
Informations- och programvisualisering
Interactive
Multivariate analysis
multivariate visualization
Optimization
Projection
Reduction
scatterplots
Studies
Three dimensional
Variables
Visualization
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Summary:Understanding three-dimensional projections created by dimensionality reduction from high-variate datasets is very challenging. In particular, classical three-dimensional scatterplots used to display such projections do not explicitly show the relations between the projected points, the viewpoint used to visualize the projection, and the original data variables. To explore and explain such relations, we propose a set of interactive visualization techniques. First, we adapt and enhance biplots to show the data variables in the projected three-dimensional space. Next, we use a set of interactive bar chart legends to show variables that are visible from a given viewpoint and also assist users to select an optimal viewpoint to examine a desired set of variables. Finally, we propose an interactive viewpoint legend that provides an overview of the information visible in a given three-dimensional projection from all possible viewpoints. Our techniques are simple to implement and can be applied to any dimensionality reduction technique. We demonstrate our techniques on the exploration of several real-world high-dimensional datasets.
ISSN:1473-8716
1473-8724
1473-8724
DOI:10.1177/1473871615600010