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A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization Engel, Daniel Hüttenberger, Lars Hamann, Bernd high-dimensional multivariate data dimension reduction manifold learning 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Daniel Engel and Lars Hüttenberger and Bernd Hamann 2012 Is Part Of OASIcs, Volume 27, Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011 InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.VLUDS.2011.135 urn:nbn:de:0030-drops-37475 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2011.135 eng https://creativecommons.org/licenses/by-nd/3.0/legalcode