Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport

  • Leo GautheronEmail author
  • Ievgen Redko
  • Carole Lartizien
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)


In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical analysis of optimal transport in domain adaptation and show that it can directly suggest a feature selection procedure leveraging the shift between the domains. Based on this, we propose a novel algorithm that aims to sort features by their similarity across the source and target domains, where the order is obtained by analyzing the coupling matrix representing the solution of the proposed optimal transportation problem. We evaluate our method on a well-known benchmark data set and illustrate its capability of selecting correlated features leading to better classification performances. Furthermore, we show that the proposed algorithm can be used as a pre-processing step for existing domain adaptation techniques ensuring an important speed-up in terms of the computational time while maintaining comparable results. Finally, we validate our algorithm on clinical imaging databases for computer-aided diagnosis task with promising results. Code related to this paper is available at: and Data related to this paper is available at:

Supplementary material

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Supplementary material 1 (pdf 339 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Leo Gautheron
    • 1
    • 2
    Email author
  • Ievgen Redko
    • 1
  • Carole Lartizien
    • 1
  1. 1.Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint-Etienne CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621LyonFrance
  2. 2.Univ Lyon, UJM-Saint-Etienne, CNRS, Institut d Optique Graduate School Laboratoire Hubert Curien UMR 5516, F-42023Saint-EtienneFrance

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