Multi-target regression via self-parameterized Lasso and refactored target space

Multi-target regression (MTR) aims at simultaneously predicting multiple continuous target variables based on the same set of input variables. It has been used to solve some challenging problems. A self-parameterized Lasso for MTR is proposed in this paper, which is applied to refactored target space via linear combinations of existing targets. Our approach can simultaneously model intrinsic inter-target correlations and input-target correlations, which makes full use of the information contained in the data. Meanwhile, this information helps automatically generate the parameters needed in the model. Compared with the common method, which requires a manual setting of parameters and is expensive to optimize these parameters, our method can save a lot of time while no reduction in performance. Besides, our method can be used not only for MTR tasks but also for multi-classification tasks. The experimental results show that our method performs well in different tasks and has a wide range of applications.