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Neural Processing Letters

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06-04-2017

Multi-Domain Transfer Component Analysis for Domain Generalization

Authors: Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes

Published in: Neural Processing Letters | Issue 3/2017

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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.

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Title: Multi-Domain Transfer Component Analysis for Domain Generalization
Authors: Thomas Grubinger
Adriana Birlutiu
Holger Schöner
Thomas Natschläger
Tom Heskes
Publication date: 06-04-2017
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2017
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-017-9612-8

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