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Active Transfer Learning
IEEE Transactions on Circuits and Systems for Video Technology ( IF 8.3 ) Pub Date : 2019-01-01 , DOI: 10.1109/tcsvt.2019.2900467
Zhihao Peng , Wei Zhang , Na Han , Xiaozhao Fang , Peipei Kang , Luyao Teng

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods.

中文翻译:

主动迁移学习

数据挖掘和机器学习的一个主要假设是训练集和测试集来自同一个域。它们共享相同的特征空间并具有相同的分布。然而,在许多实际应用中,训练集和测试集通常来自不同的领域。因此,不同域之间可能存在负相似性,从而可能发生由负相似性引起的负迁移问题。在本文中,我们提出了一种名为主动迁移学习(ATL)的新方法来解决上述问题。具体来说,引入正交投影矩阵和权重系数向量来扩展最大平均差异(MMD),使其可以最小化MMD,同时消除负转移。为了从源域中找到信息丰富和有区别的子集,然后我们通过使用源样本的局部几何结构信息提出一个信息多样性项。此外,通过使用源样本的标签信息,我们的方法可以保证所选择的子集尽可能具有判别性。最后,为了有效地实现所提出的方法,设计了一种基于乘法器交替方向法(ADMM)的交替优化方法来解决优化问题。为了证明所提出的 ATL 模型的有效性,在五个真实世界的数据集上进行了实验。实验结果表明我们的方法优于最先进的方法。我们的方法可以保证选择的子集尽可能具有判别性。最后,为了有效地实现所提出的方法,设计了一种基于乘法器交替方向法(ADMM)的交替优化方法来解决优化问题。为了证明所提出的 ATL 模型的有效性,在五个真实世界的数据集上进行了实验。实验结果表明我们的方法优于最先进的方法。我们的方法可以保证选择的子集尽可能具有判别性。最后,为了有效地实现所提出的方法,设计了一种基于乘法器交替方向法(ADMM)的交替优化方法来解决优化问题。为了证明所提出的 ATL 模型的有效性,在五个真实世界的数据集上进行了实验。实验结果表明我们的方法优于最先进的方法。实验在五个真实世界的数据集上进行。实验结果表明我们的方法优于最先进的方法。实验在五个真实世界的数据集上进行。实验结果表明我们的方法优于最先进的方法。
更新日期:2019-01-01
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