Learning Task Relational Structure for Multi-task Feature Learning
Learning Task Relational Structure for Multi-task Feature Learning
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DOI：
10.1109/icdm.2016.0166
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
De Wang;F. Nie;Heng Huang
中科院分区：
文献类型：
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作者：
De Wang;F. Nie;Heng Huang
在多任务学习中，发现任务的关系结构并利用学习到的任务结构至关重要。以往的工作都是利用低秩的潜在特征子空间来获取任务之间的关系，其中一些工作的目的是学习基于组的任务关系结构。然而，在许多情况下，对于特定的任务组，低秩子空间可能不存在，因此使用这种范例将不起作用。为了发现任务之间的关系结构，我们提出了一种新的多任务学习方法，使用结构化稀疏诱导范数来自动发现任务之间的关系。我们的新模型使用了一个更有意义的假设，即来自同一关系组的任务应该共享共同的特征子空间，而不是施加低秩约束。该方法可以发现任务的组关系结构，学习每个任务组的共享特征子空间，从而提高预测性能。我们提出的算法避免了整数规划的高计算复杂度，因此它收敛非常快。对合成数据和真实数据进行的实证研究表明，我们的方法始终优于相关的多任务学习方法。
In multi-task learning, it is paramount to discover the relational structure of tasks and utilize the learned task structure. Previous works have been using the low-rank latent feature subspace to capture the task relations, and some of them aim to learn the group based relational structure of tasks. However, in many cases, the low-rank subspace may not exist for the specific group of tasks, thus using this paradigm would not work. To discover the task relational structures, we propose a novel multi-task learning method using the structured sparsity-inducing norms to automatically uncover the relations of tasks. Instead of imposing the low-rank constraint, our new model uses a more meaningful assumption, in which the tasks from the same relational group should share the common feature subspace. We can discover the group relational structure of tasks and learn the shared feature subspace for each task group, which help to improve the predictive performance. Our proposed algorithm avoids the high computational complexity of integer programming, thus it converges very fast. Empirical studies conducted on both synthetic and real-world data show that our method consistently outperforms related multi-task learning methods.