Visual Analysis of High-Dimensional Spaces
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Beschreibung
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.
Autor*in
Georgia P. C. de Albuquerque Richers