Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en Engel, Daniel; Hüttenberger, Lars; Hamann, Bernd http://www.dagstuhl.de/oasics License
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URN: urn:nbn:de:0030-drops-37475
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A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization

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Abstract

Dimension reduction is commonly defined as the process of mapping high-dimensional data to a lower-dimensional embedding. Applications of dimension reduction include, but are not limited to, filtering, compression, regression, classification, feature analysis, and visualization. We review methods that compute a point-based visual representation of high-dimensional data sets to aid in exploratory data analysis. The aim is not to be exhaustive but to provide an overview of basic approaches, as well as to review select state-of-the-art methods. Our survey paper is an introduction to dimension reduction from a visualization point of view. Subsequently, a comparison of state-of-the-art methods outlines relations and shared research foci.

BibTeX - Entry

@InProceedings{engel_et_al:OASIcs:2012:3747,
  author =	{Daniel Engel and Lars H{\"u}ttenberger and Bernd Hamann},
  title =	{{A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization}},
  booktitle =	{Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011},
  pages =	{135--149},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-46-0},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{27},
  editor =	{Christoph Garth and Ariane Middel and Hans Hagen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2012/3747},
  URN =		{urn:nbn:de:0030-drops-37475},
  doi =		{http://dx.doi.org/10.4230/OASIcs.VLUDS.2011.135},
  annote =	{Keywords: high-dimensional, multivariate data, dimension reduction, manifold learning}
}

Keywords: high-dimensional, multivariate data, dimension reduction, manifold learning
Seminar: Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011
Issue date: 2012
Date of publication: 2012


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