| Calibrated multi-task learning | |
| Nie, Feiping1; Hu, Zhanxuan1; Li, Xuelong2 | |
| 2018-07-19 | |
| 会议名称 | 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 |
| 会议录名称 | KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| 页码 | 2012-2021 |
| 会议日期 | 2018-08-19 |
| 会议地点 | London, United kingdom |
| 出版者 | Association for Computing Machinery |
| 产权排序 | 2 |
| 摘要 | This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the nuclear norm, NC-CMTL adopts a non-convex low rank regularizer to explore the shared information among different tasks. In addition, considering that the regularization parameter for each regression task desponds on its noise level, we replace the least squares loss function by square-root loss function. Computationally, as proposed model has a non-smooth loss function and a non-convex regularization term, we construct an efficient re-weighted method to optimize it. Theoretically, we first present the convergence analysis of constructed method, and then prove that the derived solution is a stationary point of original problem. Particularly, the regularizer and optimization method used in this paper are also suitable for other rank minimization problems. Numerical experiments on both synthetic and real data illustrate the advantages of NC-CMTL over several state-of-the-art methods. © 2018 Association for Computing Machinery. |
| 作者部门 | 光学影像学习与分析中心 |
| DOI | 10.1145/3219819.3219951 |
| 收录类别 | EI |
| ISBN号 | 9781450355520 |
| 语种 | 英语 |
| EI入藏号 | 20183405705514 |
| 引用统计 | |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/30575 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.School of Computer Science, Center for OPTIMAL, Northwestern Polytechnical University, Xi'an, China; 2.OPTIMAL, Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China |
| 推荐引用方式 GB/T 7714 | Nie, Feiping,Hu, Zhanxuan,Li, Xuelong. Calibrated multi-task learning[C]:Association for Computing Machinery,2018:2012-2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Calibrated multi-tas（1278KB） | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 | ||
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