Authors: Dirk Joachim Lehmann, Holger Theisel
Abstract: Finding good projections of high-dimensional data sets into a 2D visualization domain is one of the most important prob- lems in Information Visualization. Users are interested in getting a maximal insight into the data by exploring a minimal number of projections. However, if the number is too small or improper projections are used, then important data patterns might be overlooked. We propose a data-driven approach to find minimal sets of projections that uniquely show certain data patterns. For this we introduce a distance measure of data projections that discards affine transformations of projections and this way prevents to view repetitions of the same data patterns. Based on this, we provide complete data tours of at most n/2 projections. Furthermore, we propose optimal paths of projection matrices for an interactive data exploration. We illustrate our technique with a set of state-of-the-art real high-dimensional benchmark data sets.