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Studying bias in visual features through the lens of optimal transport

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Simone Fabbrizzi
  • Xuan Zhao
  • Emmanouil Krasanakis
  • Symeon Papadopoulos
  • Eirini Ntoutsi

Research Organisations

External Research Organisations

  • Center For Research And Technology - Hellas
  • SCHUFA Holding AG
  • University of Tübingen
  • Universität der Bundeswehr München

Details

Original languageEnglish
Pages (from-to)281–312
Number of pages32
JournalData Mining and Knowledge Discovery
Volume38
Early online date2 Sept 2023
Publication statusPublished - Jan 2024

Abstract

In this article the statement after Equation 1 had an error in the published version. Please refer the correction as follows: “where ν = T#µ and T# is the push-forward of µ along the function T : X → Y” was incorrectly written as “where T# is the push-forward of µ along the function T : X → Y. Furthermore, Equation 1 itself was incorrectly formulated. Namely, the integral should have been over X and not over X × Y. The original article has been corrected.

ASJC Scopus subject areas

Cite this

Studying bias in visual features through the lens of optimal transport. / Fabbrizzi, Simone; Zhao, Xuan; Krasanakis, Emmanouil et al.
In: Data Mining and Knowledge Discovery, Vol. 38, 01.2024, p. 281–312.

Research output: Contribution to journalArticleResearchpeer review

Fabbrizzi S, Zhao X, Krasanakis E, Papadopoulos S, Ntoutsi E. Studying bias in visual features through the lens of optimal transport. Data Mining and Knowledge Discovery. 2024 Jan;38:281–312. Epub 2023 Sept 2. doi: 10.1007/s10618-023-00972-2, 10.1007/s10618-023-00986-w
Fabbrizzi, Simone ; Zhao, Xuan ; Krasanakis, Emmanouil et al. / Studying bias in visual features through the lens of optimal transport. In: Data Mining and Knowledge Discovery. 2024 ; Vol. 38. pp. 281–312.
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