Fselector: Variable selection using visual features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visual representation of large datasets should allow us to focus on essential dimensions when restricted to limited visual space. This paper presents an approach for abstracting multi-dimensional data with a focus on grouping the individual attributes based on visual features (or Scagnostics) such as density, skewness, shape, outliers, and texture. Working directly with these visual characterizations, we propose a prototype, called FSelector, to guide users when interactively exploring high dimensional datasets. In particular, selected (leading) variables are organized in a grid layout, allowing users to rapidly identify interesting pairs of variables and to focus on analyzing the original variables directly.

Original languageEnglish
Title of host publicationProceedings of Graphics Interface 2019
EditorsAndrea Tagliasacchi, Robert J. Teather
PublisherCanadian Information Processing Society
ISBN (Electronic)9780994786845
StatePublished - 2019
Event45th Graphics Interface, GI 2019 - Kingston, Canada
Duration: May 28 2019May 31 2019

Publication series

NameProceedings - Graphics Interface
Volume2019-May
ISSN (Print)0713-5424

Conference

Conference45th Graphics Interface, GI 2019
CountryCanada
CityKingston
Period05/28/1905/31/19

Keywords

  • Human-centered computing
  • Information Visualization
  • Scagnostics
  • Visualization techniques

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  • Cite this

    Dang, T. (2019). Fselector: Variable selection using visual features. In A. Tagliasacchi, & R. J. Teather (Eds.), Proceedings of Graphics Interface 2019 (Proceedings - Graphics Interface; Vol. 2019-May). Canadian Information Processing Society.