Factorial Multi-Task Learning : A Bayesian Nonparametric Approach
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach
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发表时间：
2013-06
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通讯作者：
S. Gupta;Dinh Q. Phung;S. Venkatesh
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作者：
S. Gupta;Dinh Q. Phung;S. Venkatesh
多任务学习是一种通过联合学习来提高相关任务绩效的范式。然而，对于真实世界的数据，它通常是很难评估的任务相关性和联合学习无关的任务可能会导致严重的性能下降。为此，我们提出了一个框架，分组的任务，根据他们的相关性在一个子空间，并允许不同程度的相关性任务之间共享的子空间基地跨组。这提供了当两组任务不相关时不共享以及当任务相关时部分/全部共享的灵活性。重要的是，任务组的数量和子空间维数是从数据中自动推断出来的。为了实现我们的框架，我们引入了一种新的贝叶斯非参数先验，扩展了传统的分层beta过程之前使用的Dirichlet过程，允许潜在的无限数量的儿童beta过程。我们将我们的模型应用于多任务回归和分类应用。使用几个合成和真实的数据集的实验结果表明，我们的模型的优越性，最近的多任务学习方法。
Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods.