Name
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 demonstrate that Multi-TCA can improve predictive performance on previously unseen domains. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19258-1_28 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I (IWANN 2015) |
Field | DocType | Volume |
Subspace topology,Computer science,Domain adaptation,Transfer of learning,Artificial intelligence,Original Application,Component analysis,Machine learning | Conference | 9094 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.38 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Grubinger | 1 | 9 | 1.52 |
| Adriana Birlutiu | 2 | 70 | 6.41 |
| Holger Schöner | 3 | 2 | 0.71 |
| T Natschläger | 4 | 1199 | 102.98 |
| Tom Heskes | 5 | 1519 | 198.44 |