Dimensionality Reduction From Several Angles

  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 judgments 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. Bio: Tamara Munzner is a Professor of Computer Science at the University of British Columbia, in the research area of information visualization. She has been active in visualization research since 1991, and has published over fifty papers and book chapters. Before earning a PhD from Stanford in 2000, she was a technical staff member at the NSF-funded Geometry Center at the University of Minnesota for four years. Afterwards, she was a research scientist at the Compaq Systems Research Lab from 2000 to 2002. She co-chaired InfoVis in 2003 and 2004 and EuroVis in 2009 and 2010. She is currently on the InfoVis Steering Committee and the VIS Executive Committee, was a founding member of the BioVis Steering Committee from 2010 through 2013, and was a Member At Large of the Executive Committee of the IEEE Visualization and Graphics Technical Committee from 2004-2009. She has consulted for companies including Silicon Graphics, Microsoft, and early-stage startups. Her research interests include the development, evaluation, and characterization of information visualization systems and techniques from both problem-driven and technique-driven perspectives. She has worked on problem-driven visualization projects in a broad range of application domains, including genomics, evolutionary biology, large-scale system administration, web log analysis, computational linguistics, and geometric topology. Her technique-driven interests include dimensionality reduction and graph drawing.