Robust learning with imperfect privileged information
Robust learning with imperfect privileged information
复制标题
不完美特权信息的鲁棒学习
DOI：
10.1016/j.artint.2020.103246
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发表时间：
2020-05-01
影响因子：
14.4
通讯作者：
Tao, Dacheng
中科院分区：
文献类型：
--
作者：
Li, Xue;Du, Bo;Tao, Dacheng
在使用特权信息学习（LUPI）范式中，示例数据并不总是干净的，而收集到的特权信息在实践中也可能是不完善的。这里，不完全特权信息可以指不总是准确或受噪声干扰的辅助信息，也可以指不完全特权信息，其中特权信息仅对部分训练数据可用。由于缺乏明确的策略来处理示例数据中的噪声和不完善的特权信息，现有的使用特权信息学习（LUPI）方法可能会遇到严重的问题。因此，本文提出了一种鲁棒支持向量机+方法来处理LUPI中的不完全数据。为了使SVM+模型对样本数据和特权信息中的噪声具有鲁棒性，在严格的理论分析基础上，鲁棒SVM+最大化了可能影响判断的扰动的下界。此外，为了处理不完整的特权信息，我们利用现有的特权信息来帮助我们逼近训练数据中缺失的特权信息。采用基于迭代部署现成二次规划求解器和乘法器交替方向法（ADMM）技术的两步交替优化策略，可以有效地解决该方法的优化问题。在实际数据集上的综合实验证明了所提出的鲁棒支持向量机+方法在处理不完全特权信息方面的有效性。(C) 2020 Elsevier B.V.版权所有
In the learning using privileged information (LUPI) paradigm, example data cannot always be clean, while the gathered privileged information can be imperfect in practice. Here, imperfect privileged information can refer to auxiliary information that is not always accurate or perturbed by noise, or alternatively to incomplete privileged information, where privileged information is only available for part of the training data. Because of the lack of clear strategies for handling noise in example data and imperfect privileged information, existing learning using privileged information (LUPI) methods may encounter serious issues. Accordingly, in this paper, we propose a Robust SVM+ method to tackle imperfect data in LUPI. In order to make the SVM+ model robust to noise in example data and privileged information, Robust SVM+ maximizes the lower bound of the perturbations that may influence the judgement based on a rigorous theoretical analysis. Moreover, in order to deal with the incomplete privileged information, we use the available privileged information to help us in approximating the missing privileged information of training data. The optimization problem of the proposed method can be efficiently solved by employing a two-step alternating optimization strategy, based on iteratively deploying off-the-shelf quadratic programming solvers and the alternating direction method of multipliers (ADMM) technique. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the proposed Robust SVM+ method in handling imperfect privileged information. (C) 2020 Elsevier B.V. All rights reserved.