Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisations

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Date
2010
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Volume Title
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The Eurographics Association
Abstract
Challenges in Visual Analytics frequently involve massive repositories, which do not only contain a large number of information artefacts, but also a high number of relevant dimensions per artefact. Dimensionality reduction algorithms are commonly used to transform high-dimensional data into low- dimensional representations which are suitable for visualisation purposes. For example, Information Landscapes visualise high-dimensional data in two dimensions using distance-preserving projection methods. The inaccuracies introduced by such methods are usually expressed through a global stress measure which does not provide insight into localised phenomena. In this paper, we propose the use of Stress Maps, a combination of heat maps and information landscapes, to support algorithm development and optimization based on local stress measures. We report on an application of Stress Maps to a scalable text projection algorithm and describe two categories of problems related to localised stress phenomena which we have identified using the proposed method.
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@inproceedings{
:10.2312/PE/EuroVAST/EuroVAST10/013-018
, booktitle = {
EuroVAST 2010: International Symposium on Visual Analytics Science and Technology
}, editor = {
Joern Kohlhammer and Daniel Keim
}, title = {{
Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisations
}}, author = {
Seifert, Christin
and
Sabol, Vedran
and
Kienreich, Wolfgang
}, year = {
2010
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-905673-74-6
}, DOI = {
/10.2312/PE/EuroVAST/EuroVAST10/013-018
} }
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