Manifold Regularized Multi-task Learning
文献类型：会议论文
| 作者 | Yang, Peipei ; Zhang, Xu-Yao; Huang, Kaizhu; Liu, Cheng-Lin
|
| 出版日期 | 2012-11 |
| 会议日期 | 2012-11-12 |
| 会议地点 | Doha, Qatar |
| 关键词 | Multi-task Learning Manifold Learning Laplacian |
| DOI | 10.1007/978-3-642-34487-9_64 |
| 英文摘要 | Multi-task learning (MTL) has drawn a lot of attentions in machine learning. By training multiple tasks simultaneously, information can be better shared across tasks. This leads to significant performance improvement in many problems. However, most existing methods assume that all tasks are related or their relationship follows a simple and specified structure. In this paper, we propose a novel manifold regularized framework for multi-task learning. Instead of assuming simple relationship among tasks, we propose to learn task decision functions as well as a manifold structure from data simultaneously. As manifold could be arbitrarily complex, we show that our proposed framework can contain many recent MTL models, e.g. RegMTL and cCMTL, as special cases. The framework can be solved by alternatively learning all tasks and the manifold structure. In particular, learning all tasks with the manifold regularization can be solved as a single-task learning problem, while the manifold structure can be obtained by successive Bregman projection on a convex feasible set. On both synthetic and real datasets, we show that our method can outperform the other competitive methods. |
| 会议录 | 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12501]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Huang, Kaizhu |
| 作者单位 | National Laboratory of Pattern Recognition |
| 推荐引用方式 GB/T 7714 | Yang, Peipei,Zhang, Xu-Yao,Huang, Kaizhu,et al. Manifold Regularized Multi-task Learning[C]. 见:. Doha, Qatar. 2012-11-12. |
入库方式： OAI收割
来源：自动化研究所
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