Temporal scatterplots

Or Patashnik, Min Lu*, Amit H. Bermano, Daniel Cohen-Or

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.

Original languageEnglish
Pages (from-to)385-400
Number of pages16
JournalComputational Visual Media
Volume6
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • principle component analysis (PCA)
  • scatterplot
  • temporal data
  • visual clutter

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