通过学习任务关系迁移度量学习

Yu Zhang, D. Yeung
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引用次数: 102

摘要

距离度量学习在许多数据挖掘算法中起着至关重要的作用,因为算法的性能很大程度上依赖于选择一个好的度量。然而,在许多应用程序中可用的标记数据是稀缺的,因此学到的度量通常是不令人满意的。在本文中,我们考虑了一种迁移学习设置,其中一些相关的带有标记数据的源任务可以帮助目标任务的学习。我们首先通过以任务协方差矩阵的形式对任务关系进行建模,提出了一个多任务度量学习的凸公式。然后,我们将迁移学习视为多任务学习的一个特例,并将多任务度量学习的公式调整到我们的迁移学习方法的设置中,称为迁移度量学习(TML)。在TML中,我们在一个统一的凸公式下学习源任务和目标任务之间的度量和任务协方差。为了解决凸优化问题,我们使用交替方法,其中每个子问题都有一个有效的解。在一些常用的迁移学习应用上的实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer metric learning by learning task relationships
Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory. In this paper, we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multi-task learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method.
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