Domain generalization based on transfer component analysis

T. Grubinger, A. Birlutiu, H. Schöner, T. Natschläger, T. Heskes. Domain generalization based on transfer component analysis. volume 9094, pages 325-334, 6, 2015.

  • Thomas Grubinger
  • Adriana Birlutiu
  • Holger Schöner
  • Thomas Natschläger
  • Tom Heskes
  • I. Rojas
  • G. Joya
  • A. Catala
BuchAdvances in Computational Intelligence - Proc. IWANN 2015, Part I
TypIn Konferenzband
SerieLecture Notes in Computer Science

This paper investigates domain generalization: How to use knowledge acquired from related domains and apply it to new domains? Transfer Component Analysis (TCA) learns a shared subspace by minimizing the dissimilarities across domains, while maximally preserving the data variance. We propose Multi-TCA, an extension of TCA to multiple domains as well as Multi-SSTCA, which is an extension of TCA for semi-supervised learning. In addition to the original application of TCA for domain adaptation problems, we show that Multi-TCA can also be applied for domain generalization. Multi-TCA and Multi-SSTCA are evaluated on two publicly available datasets with the tasks of landmine detection and Parkinson telemonitoring. Experimental results demon-strate that Multi-TCA can improve predictive performance on previously unseen domains.