Dimensionality Reduction From Several Angles, Tamara Munzner
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I will present several projects that attack the problem of dimensionality reduction (DR) in visualization from different methodological angles of attack, in order to answer different kinds of questions. First, can we design better DR algorithms? Glimmer is a multilevel multidimensional scaling (MDS) algorithm that exploits the GPU. Glint is a new MDS framework that achieves high performance on costly distance functions. Second, can we build a DR system for real people? DimStiller is a toolkit for DR that provides local and global guidance to users who may not be experts in the mathematics of high-dimensional data analysis, in hopes of "DR for the rest of us". Third, how should we show people DR results? An empirical lab study provides guidance on visual encoding for system developers, showing that points are more effective than spatialized landscapes for visual search tasks with DR data. A data study, where a small number of people make judgements about a large number of datasets rather than vice versa as with a typical user study, produced a taxonomy of visual cluster separation factors. Fourth, when do people need to use DR? Sometimes it is not the right solution, as we found when grappling with the design of the QuestVis system for a environmental sustainability simulation. We provide guidance for researchers and practitioners engaged in this kind of problem-driven visualization work with the nested model of visualization design and evaluation and the nine-stage framework for design study methodology. Much of this work was informed by preliminary results from an ongoing project, a two-year qualitative study of high-dimensional data analysts in many domains, to discover how the use of DR "in the wild" may or may not match up with the assumptions that underlie previous algorithmic work.