Multitask transfer learning with kernel representation

In many real-world applications, collecting and labeling the data is expensive and time-consuming. Thus, there is a need to obtain a high-performance learner by leveraging the data or knowledge from other domains. Transfer learning is a promising method to solve the above problems. In this paper, we propose a multitask transfer learning method, which aims to improve the performance of the target learner by transferring knowledge from the related source tasks. First, we formulate the target learner as a nonlinear function, which is approximated by the linear combination of the eigenfunctions. Further, to transfer knowledge from the source tasks, we constrain the target model to be the linear combination of the source models according to the previous work. However, knowledge from some source tasks may not be useful for adaptation, so we add a sparse constraint to the objective function to select the related source tasks. Different from previous transfer learning methods, our method transfers knowledge by jointly learning the source tasks and the target task. Besides, it can select the source tasks associated with the target task by the sparse constraint. Empirically, the method exhibits protection against negative transfer. Finally, we compare our proposed method with three single-task learning methods and six state-of-the-art multitask learning methods on two data sets. When compared with the second best results, the nMSE of our method achieves a relative improvement of \(10.85\%\) with a training size of 100 on the SARCOS data set and a relative improvement of \(4.26\%\) with a training ratio of \(20\%\) on the Isolet data set. Experimental results show that our proposed method can effectively improve the performance of the target task by transferring knowledge from the related source tasks.