UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

Mateus Espadoto, Gabriel Appleby, Ashley Suh, Dylan Cashman, Mingwei Li, Carlos Scheidegger, Erik W. Anderson, Remco Chang, Alexandru C. Telea

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection - the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

Original languageEnglish (US)
Pages (from-to)1559-1572
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume29
Issue number2
DOIs
StatePublished - Feb 1 2023

Keywords

  • Multidimensional data
  • back-projection
  • inverse-projection
  • multidimensional projection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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