Domain generalization based on transfer component analysis

Autoren Thomas Grubinger
Adriana Birlutiu
Holger Schöner
Thomas Natschläger
Tom Heskes
Editoren I. Rojas
G. Joya
A. Catala
TitelDomain generalization based on transfer component analysis
BuchtitelAdvances in Computational Intelligence - Proc. IWANN 2015, Part I
Typin Konferenzband
VerlagSpringer
SerieLecture Notes in Computer Science
Band9094
ISBN978-3-319-19257-4
MonatJune
Jahr2015
Seiten325-334
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.