Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Dominik Sacha, Leishi Zhang, Michael Sedlmair, John A. Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C. North, Daniel A. Keim

Research output: Contribution to journalArticleScientificpeer-review

105 Citations (Scopus)

Abstract

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a 'human in the loop' process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

Original languageEnglish
Article number7536217
Pages (from-to)241-250
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume23
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • dimensionality reduction
  • Interactive visualization
  • machine learning
  • visual analytics

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