Visual analysis of high-dimensional spaces

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The visual exploration and analysis of high-dimensional data sets commonly requires projecting the data into lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even infeasible. In this thesis I present automatic algorithms to compute visual quality metrics and show different situations where they can be used to support the analysis of high-dimensional data sets. The proposed methods can be applied to different specific user tasks and can be combined with established visualization techniques to sort or select projections of the data based on their information-bearing content. These approaches can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. Additionally, I present a framework designed to generate synthetic data, where users can interactively create and navigate through high dimensional data sets.