t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
Original languageEnglish
Article number9064929
Pages (from-to)2696-2714
Number of pages19
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number8
DOIs
Publication statusPublished - 1 Aug 2020

Keywords

  • Tools
  • Visualization
  • Data visualization
  • Task analysis
  • Correlation
  • Principal component analysis
  • Dimensionality reduction

Fingerprint

Dive into the research topics of 't-viSNE: Interactive Assessment and Interpretation of t-SNE Projections'. Together they form a unique fingerprint.

Cite this