File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.24963/ijcai.2017/454
- Scopus: eid_2-s2.0-85031892773
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Learning discriminative correlation subspace for heterogeneous domain adaptation
Title | Learning discriminative correlation subspace for heterogeneous domain adaptation |
---|---|
Authors | |
Issue Date | 2017 |
Citation | 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19-25 August 2017. In Conference Proceedings, 2017, p. 3252-3258 How to Cite? |
Abstract | Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods. |
Persistent Identifier | http://hdl.handle.net/10722/276556 |
ISSN | 2020 SCImago Journal Rankings: 0.649 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yan, Yuguang | - |
dc.contributor.author | Li, Wen | - |
dc.contributor.author | Ng, Michael | - |
dc.contributor.author | Tan, Mingkui | - |
dc.contributor.author | Wu, Hanrui | - |
dc.contributor.author | Min, Huaqing | - |
dc.contributor.author | Wu, Qingyao | - |
dc.date.accessioned | 2019-09-18T08:33:58Z | - |
dc.date.available | 2019-09-18T08:33:58Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19-25 August 2017. In Conference Proceedings, 2017, p. 3252-3258 | - |
dc.identifier.issn | 1045-0823 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276556 | - |
dc.description.abstract | Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods. | - |
dc.language | eng | - |
dc.relation.ispartof | International Joint Conference on Artificial Intelligence (IJCAI) | - |
dc.title | Learning discriminative correlation subspace for heterogeneous domain adaptation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.24963/ijcai.2017/454 | - |
dc.identifier.scopus | eid_2-s2.0-85031892773 | - |
dc.identifier.spage | 3252 | - |
dc.identifier.epage | 3258 | - |
dc.identifier.issnl | 1045-0823 | - |