Neural Processing Letters

, Volume 46, Issue 3, pp 845–855 | Cite as

Multi-Domain Transfer Component Analysis for Domain Generalization

  • Thomas GrubingerEmail author
  • Adriana Birlutiu
  • Holger Schöner
  • Thomas Natschläger
  • Tom Heskes


This paper presents the domain generalization methods Multi-Domain Transfer Component Analysis (Multi-TCA) and Multi-Domain Semi-Supervised Transfer Component Analysis (Multi-SSTCA) which are extensions of the domain adaptation method Transfer Component Analysis to multiple domains. Multi-TCA learns a shared subspace by minimizing the dissimilarities across domains, while maximally preserving the data variance. The proposed methods are compared to other state-of-the-art methods on three public datasets and on a real-world case study on climate control in residential buildings. Experimental results demonstrate that Multi-TCA and Multi-SSTCA can improve predictive performance on previously unseen domains. We perform sensitivity analysis on model parameters and evaluate different kernel distances, which facilitate further improvements in predictive performance.


Domain generalization Domain adaptation Transfer learning Transfer component analysis 


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Data Analysis SystemsSoftware Competence Center HagenbergHagenberg im MühlkreisAustria
  2. 2.Faculty of Science“1 Decembrie 1918” University of Alba-IuliaAlba IuliaRomania
  3. 3.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

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