A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization
Daniel Engel, Lars Hüttenberger, Bernd Hamann · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2012
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.