Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. A characteristic of network lasso is setting a different model for each sample. The relationships among the models are represented by relational coefficients. A crucial issue in network lasso is to provide appropriate values for these relational coefficients. In this paper, we propose a Bayesian approach to solve multi-task learning problems by network lasso. This approach allows us to objectively determine the relational coefficients by Bayesian estimation. The effectiveness of the proposed method is shown in a simulation study and a real data analysis.
翻译:网络拉索是通过固定化最大可能性方法解决多任务学习问题的一种方法。 网络拉索的一个特征是为每个样本设定一个不同的模型。 模型之间的关系由关系系数代表。 网络拉索的一个关键问题是为这些关系系数提供恰当的数值。 在本文件中,我们提出一种巴伊西亚方法,通过网络拉索解决多任务学习问题。 这种方法使我们能够客观地确定Bayesian估计的关联系数。 模拟研究和真实数据分析显示拟议方法的有效性。