https://github.com/computorg/published-paper-tsne

A prototype of published Computo paper (using quarto)
https://github.com/computorg/published-paper-tsne

interactive-visualizations mock-paper

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A prototype of published Computo paper (using quarto)

• Host: GitHub
• URL: https://github.com/computorg/published-paper-tsne
• Owner: computorg
• License: cc-by-4.0
• Created: 2021-08-25T12:19:14.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2026-07-07T09:42:59.000Z (1 day ago)
• Last Synced: 2026-07-07T10:26:26.120Z (1 day ago)
• Topics: interactive-visualizations, mock-paper
• Language: TeX
• Homepage: http://computo-journal.org/published-paper-tsne/
• Size: 7.42 MB
• Stars: 1
• Watchers: 5
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Visualizing Data using t-SNE (mock contribution)

Laurens van der Maaten, Geoffrey Hinton

2008-08-11

*This page is a reworking of the original t-SNE article using the

Computo template. It aims to help authors submitting to the journal by

using some advanced formatting features. We warmly thank the authors of

t-SNE and the editor of JMLR for allowing us to use their work to

illustrate the Computo spirit.*

### Citation

Laurens van der Maaten and Geoffrey Hinton (August 2008). Visualizing Data using t-SNE (mock contribution). Computo.

### Badges

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publish](https://github.com/computorg/published-paper-tsne/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-paper-tsne/actions/workflows/build.yml)

[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-paper-tsne/issues?q=is%3Aopen+is%3Aissue+label%3Areview)

[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-paper-tsne)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-paper-tsne)

[![DOI:10.57750/xxx-xxx](https://img.shields.io/badge/DOI-10.57750%2Fxxx--xxx-034E79.svg)](https://doi.org/10.57750/xxx-xxx)

[![Creative Commons

License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)

### Authors’ affiliations

- [Laurens van der Maaten](https://lvdmaaten.github.io/) (TiCC, Tilburg University)

- [Geoffrey Hinton](https://www.cs.toronto.edu/~hinton/) (Department of Computer Science, University of Toronto)

### Abstract

We present a new technique called “t-SNE” that visualizes

high-dimensional data by giving each datapoint a location in a two or

three-dimensional map. The technique is a variation of Stochastic

Neighbor Embedding (Hinton and Roweis 2003) that is much easier to

optimize, and produces significantly better visualizations by reducing

the tendency to crowd points together in the center of the map. t-SNE is

better than existing techniques at creating a single map that reveals

structure at many different scales. This is particularly important for

high-dimensional data that lie on several different, but related,

low-dimensional manifolds, such as images of objects from multiple

classes seen from multiple viewpoints. For visualizing the structure of

very large data sets, we show how t-SNE can use random walks on

neighborhood graphs to allow the implicit structure of all the data to

influence the way in which a subset of the data is displayed. We

illustrate the performance of t-SNE on a wide variety of data sets and

compare it with many other non-parametric visualization techniques,

including Sammon mapping, Isomap, and Locally Linear Embedding. The

visualization produced by t-SNE are significantly better than those

produced by other techniques on almost all of the data sets.





Hinton, Geoffrey E, and Sam Roweis. 2003. “Stochastic Neighbor

Embedding.” In *Advances in Neural Information Processing Systems*,

edited by S. Becker, S. Thrun, and K. Obermayer, vol. 15. MIT Press.

.