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A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization

Authors Daniel Engel, Lars Hüttenberger, Bernd Hamann



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OASIcs.VLUDS.2011.135.pdf
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Daniel Engel
Lars Hüttenberger
Bernd Hamann

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Daniel Engel, Lars Hüttenberger, and Bernd Hamann. A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization. In Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011. Open Access Series in Informatics (OASIcs), Volume 27, pp. 135-149, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2012)
https://doi.org/10.4230/OASIcs.VLUDS.2011.135

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.
Keywords
  • high-dimensional
  • multivariate data
  • dimension reduction
  • manifold learning

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