Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisations
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Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
@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}
}