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t-viSNE: A Visual Inspector for the Exploration of t-SNE
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS, DISA-VAESS)ORCID iD: 0000-0002-9079-2376
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-2901-935X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-0519-2537
2018 (English)In: Presented at IEEE Information Visualization  (VIS '18), Berlin, Germany, 21-26 October, 2018, 2018Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Visualization, machine learning, visual analytics, information visualization, interaction, dimensionality reduction
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-76980OAI: oai:DiVA.org:lnu-76980DiVA, id: diva2:1234200
Conference
IEEE Information Visualization (VIS '18), Berlin, Germany, 21-26 October, 2018
Available from: 2018-07-23 Created: 2018-07-23 Last updated: 2019-01-17Bibliographically approved

Open Access in DiVA

t-viSNE_Chatzimparmpas_et_al(4285 kB)39 downloads
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File name FULLTEXT01.pdfFile size 4285 kBChecksum SHA-512
31c9f02d48f97113dfa178c47f1e6af149d4b74299276a55d7a3080337ab4dc26e469e291cfcd7a981b4115f1adb2d11c17d9e377ba2ded1213c66b8bf8f73ce
Type fulltextMimetype application/pdf

Authority records BETA

Chatzimparmpas, AngelosMartins, Rafael MessiasKerren, Andreas

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Chatzimparmpas, AngelosMartins, Rafael MessiasKerren, Andreas
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Language
  • de-DE
  • en-GB
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  • Other locale
More languages
Output format
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