Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections

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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.
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@article{
10.1111:cgf.12639
, journal = {Computer Graphics Forum}, title = {{
Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections
}}, author = {
Liu, Shusen
 and
Wang, Bei
 and
Thiagarajan, Jayaraman J.
 and
Bremer, Peer-Timo
 and
Pascucci, Valerio
}, year = {
2015
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, DOI = {
10.1111/cgf.12639
} }
Citation