Volume 14, Issue 4 e1573
Advanced Review

The state-of-the-art on tours for dynamic visualization of high-dimensional data

Stuart Lee

Stuart Lee

Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia

Contribution: Resources (equal), Visualization (equal), Writing - original draft (equal), Writing - review & editing (equal)

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Dianne Cook

Corresponding Author

Dianne Cook

Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia

Correspondence

Dianne Cook, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia.

Email: [email protected]

Contribution: Resources (equal), Supervision (equal), Visualization (equal), Writing - original draft (equal), Writing - review & editing (equal)

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Natalia da Silva

Natalia da Silva

Instituto de Estadística (IESTA), Universidad de la República, Montevideo, Uruguay

Contribution: Visualization (equal), Writing - review & editing (equal)

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Ursula Laa

Ursula Laa

Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria

Contribution: Resources (equal), Visualization (equal), Writing - original draft (equal), Writing - review & editing (equal)

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Nicholas Spyrison

Nicholas Spyrison

Faculty of Information Technology, Monash University, Melbourne, Australia

Contribution: Writing - original draft (equal)

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Earo Wang

Earo Wang

Department of Statistics, The University of Auckland, Auckland, New Zealand

Contribution: Writing - review & editing (equal)

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H. Sherry Zhang

H. Sherry Zhang

Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia

Contribution: Writing - original draft (equal)

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First published: 09 December 2021
Citations: 1

Edited by: Kimberly Sellers, Commissioning Editor and David W Scott, Editor-in-Chief

Funding information: Australian Research Council

Abstract

This article discusses a high-dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments and applications of the tour that are being found across the sciences and machine learning.

This article is categorized under:

  • Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
  • Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization
  • Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis

Graphical Abstract

Clusters revealed by t-SNE can be viewed relatively to each each other in 10D using a grand tour.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

DATA AVAILABILITY STATEMENT

The data used for examples in this paper are all publicly available, and appropriately referenced.