Domain generalization based on transfer component analysis

Authors Thomas Grubinger
Adriana Birlutiu
Holger Schöner
Thomas Natschläger
Tom Heskes
Editors I. Rojas
G. Joya
A. Catala
Title Domain generalization based on transfer component analysis
Booktitle Advances in Computational Intelligence - Proc. IWANN 2015, Part I
Type in proceedings
Publisher Springer
Series Lecture Notes in Computer Science
Volume 9094
ISBN 978-3-319-19257-4
Month June
Year 2015
Pages 325-334
SCCH ID# 1449
Abstract

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.