@Article{grubinger_multi-domain_2017,
  author   = {Grubinger, Thomas and Birlutiu, Adriana and Schöner, Holger and Natschläger, Thomas and Heskes, Tom},
  title    = {Multi-{Domain} {Transfer} {Component} {Analysis} for {Domain} {Generalization}},
  journal  = {Neural Processing Letters},
  year     = {2017},
  pages    = {1--11},
  month    = apr,
  issn     = {1370-4621, 1573-773X},
  note     = {00000},
  abstract = {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.},
  doi      = {10.1007/s11063-017-9612-8},
  file     = {GrubingerEtAl_2017_Multi-Domain_Transfer_Component_Analysis_for_Domain_Generalization.pdf:files/1057/GrubingerEtAl_2017_Multi-Domain_Transfer_Component_Analysis_for_Domain_Generalization.pdf:application/pdf;Snapshot:files/1055/s11063-017-9612-8.html:text/html},
  language = {en},
  url      = {https://link.springer.com/article/10.1007/s11063-017-9612-8},
  urldate  = {2017-08-01},
}