dtour: a Steerable Tour de Vis Through High-Dimensional Data
Authors
Fritz Lekschas (Ozette Technologies), Nezar Abdennur (UMass Chan Medical School)
Presentation
- Session
- Lost in Dimensions
- Time
- Thursday, Nov 12, 15:00 – 15:09 (US/Eastern) · session 15:00 – 16:30
- Location
- Hall Essex north
Links
Sign in to access the preprint PDF.
Sign in- Download Supplemental Material
Keywords
High-dimensional data, dimensionality reduction, tours, embedding visualization
Abstract
Understanding high-dimensional data requires projecting it into lower-dimensional spaces, but any single projection inevitably loses information or introduces distortions. Tours address this limitation through animation of 2D projection sequences, yet existing tools present tradeoffs in the freedom and steerability of projection traversal, providing little to no ability to move between expert-guided paths and unrestrained exploration. We present dtour, a tour interface that combines static projection previews, reversible scrubbing along continuous geodesic projection paths, manual projection manipulation, and a wandering grand tour, all within a single progressive exploration interface. dtour scales to millions of points via GPU-accelerated rendering, runs in any modern browser, and integrates with both Python and JavaScript ecosystems. We demonstrate dtour on text, image, and single-cell data for two usage scenarios: gradually revealing structure in high-dimensional data and validating non-linear dimensionality reduction outputs.
For Practitioners
data scientists, AI scientists, biologists, anyone working with high-dimensional data