Multi-Domain Transfer Component Analysis for Domain Generalization (bibtex)
by Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes
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.
Reference:
Multi-Domain Transfer Component Analysis for Domain Generalization (Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes), In Neural Processing Letters, 2017.
Bibtex Entry:
@article{grubinger_multi-domain_2017,
	title = {Multi-{Domain} {Transfer} {Component} {Analysis} for {Domain} {Generalization}},
	issn = {1370-4621, 1573-773X},
	url = {https://link.springer.com/article/10.1007/s11063-017-9612-8},
	doi = {10.1007/s11063-017-9612-8},
	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.},
	language = {en},
	urldate = {2017-08-01},
	journal = {Neural Processing Letters},
	author = {Grubinger, Thomas and Birlutiu, Adriana and Schöner, Holger and Natschläger, Thomas and Heskes, Tom},
	month = apr,
	year = {2017},
	pages = {1--11}
}
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