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IEEE Trans Vis Comput Graph. 2016 Jan;22(1):609-18. doi: 10.1109/TVCG.2015.2467132. Epub 2015 Aug 12.

Optimal Sets of Projections of High-Dimensional Data.

Abstract

Finding good projections of n-dimensional datasets into a 2D visualization domain is one of the most important problems in Information Visualization. Users are interested in getting 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 dissimilarity measure of data projections that discards affine transformations of projections and prevents 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 datasets.

PMID:
26390468
DOI:
10.1109/TVCG.2015.2467132

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