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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The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.
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Grubinger, T., Birlutiu, A., Schöner, H. et al. Multi-Domain Transfer Component Analysis for Domain Generalization. Neural Process Lett 46, 845–855 (2017). https://doi.org/10.1007/s11063-017-9612-8
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DOI: https://doi.org/10.1007/s11063-017-9612-8