Name
Abstract | ||
|---|---|---|
•To deal with the distribution divergence between domains, we propose a domain adaptation model UCGS based on the coupled adaptations theory. UCGS combines the inter-domain distribution divergence reduction and classifier construction in a unified model for robust transfer learning.•UCGS employs MMD to formalize the distribution divergence statistically. The means of the data distributions are well matched through minimizing MMD.•Furthermore, UCGS flexibly employs the Nyström method to explore the inter-domain geometric connections and uses the Nyström approximation error to quantify the inter-domain geometric differences. A domain-invariant graph is finally constructed to bridge two domains geometrically.•Comprehensive experiments on real-world datasets verify the superiority of UCGS. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107658 | Pattern Recognition |
Keywords | DocType | Volume |
Domain adaptation,Statistical adaptation,Maximum mean discrepancy (MMD),Geometric adaptation,Nyström method | Journal | 110 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Liu | 1 | 87 | 13.82 |
| Jinfeng Li | 2 | 1 | 1.36 |
| Bao-Di Liu | 3 | 166 | 27.34 |
| Weili Guan | 4 | 43 | 10.84 |
| Yicong Zhou | 5 | 1822 | 108.83 |
| Changsheng Xu | 6 | 4957 | 332.87 |