Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations

Rene Cutura, Michaël Aupetit, Jean Daniel Fekete, Michael Sedlmair

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

We propose Compadre, a tool for visual analysis for comparing distances of high-dimensional (HD) data and their low-dimensional projections. At the heart is a matrix visualization to represent the discrepancy between distance matrices, linked side-by-side with 2D scatterplot projections of the data. Using different examples and datasets, we illustrate how this approach fosters (1) evaluating dimensionality reduction techniques w.r.t. how well they project the HD data, (2) comparing them to each other side-by-side, and (3) evaluate important data features through subspace comparison. We also present a case study, in which we analyze IEEE VIS authors from 1990 to 2018, and gain new insights on the relationships between coauthors, citations, and keywords. The coauthors are projected as accurately with UMAP as with t-SNE but the projections show different insights. The structure of the citation subspace is very different from the coauthor subspace. The keyword subspace is noisy yet consistent among the three IEEE VIS sub-conferences.

Original languageEnglish
Title of host publicationProceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
EditorsGenny Tortora, Giuliana Vitiello, Marco Winckler
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375351
DOIs
Publication statusPublished - 28 Sept 2020
Event2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italy
Duration: 28 Sept 20202 Oct 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 International Conference on Advanced Visual Interfaces, AVI 2020
Country/TerritoryItaly
CitySalerno
Period28/09/202/10/20

Keywords

  • Dimensionality Reduction
  • High-Dimensional Data
  • Matrix Visualization
  • Subspace Analysis
  • Visual Comparison

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