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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
DOI10.1145/3219819.3219951
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
专题光学影像学习与分析中心
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
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GB/T 7714
Nie, Feiping,Hu, Zhanxuan,Li, Xuelong. Calibrated multi-task learning[C]:Association for Computing Machinery,2018:2012-2021.
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