Learning the Graph of Relations Among Multiple Tasks
Learning the Graph of Relations Among Multiple Tasks
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发表时间：
2013-10
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通讯作者：
Andreas Argyriou;S. Clémençon;Ruocong Zhang
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作者：
Andreas Argyriou;S. Clémençon;Ruocong Zhang
我们提出了多任务拉普拉斯学习，这是一种联合学习紧密相关任务集群的新方法。与标准多任务方法不同，任务之间的关系图并不假定是先验已知的，而是通过多任务拉普拉斯算法学习的。该算法建立在基于内核的方法之上，并利用优化方法来学习连续参数化的内核。它涉及求解特定类型的半定规划，为此我们开发了一种基于 Douglas-Rachford 分裂方法的算法。多任务拉普拉斯学习可以在许多情况下得到应用，在这些情况下，任务之间有不同程度的关联，有些是强关联，有些是弱关联。我们的实验强调了多任务拉普拉斯学习优于任务独立学习和最先进的多任务学习方法的情况。此外，他们还证明了我们的算法将任务划分为集群，每个集群都包含相关性良好的任务。
We propose multitask Laplacian learning, a new method for jointly learning clusters of closely related tasks. Unlike standard multitask methodologies, the graph of relations among the tasks is not assumed to be known a priori, but is learned by the multitask Laplacian algorithm. The algorithm builds on kernel based methods and exploits an optimization approach for learning a continuously parameterized kernel. It involves solving a semidefinite program of a particu- lar type, for which we develop an algorithm based on Douglas-Rachford split- ting methods. Multitask Laplacian learning can find application in many cases in which tasks are related with each other to varying degrees, some strongly, oth- ers weakly. Our experiments highlight such cases in which multitask Laplacian learning outperforms independent learning of tasks and state of the art multitask learning methods. In addition, they demonstrate that our algorithm partitions the tasks into clusters each of which contains well correlated tasks.