Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s11063-017-9612-8 | Neural Processing Letters |
Keywords | Field | DocType |
Domain generalization,Domain adaptation,Transfer learning,Transfer component analysis | Kernel (linear algebra),Data mining,Subspace topology,Computer science,Domain adaptation,Transfer of learning,Multi domain,Artificial intelligence,Component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 3 | 1370-4621 |
Citations | PageRank | References |
4 | 0.40 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Grubinger | 1 | 7 | 0.84 |
| Adriana Birlutiu | 2 | 70 | 6.41 |
| Holger Schöner | 3 | 4 | 0.40 |
| T Natschläger | 4 | 1199 | 102.98 |
| Tom Heskes | 5 | 1519 | 198.44 |