An efficient multiple kernel computation method for regression analysis of economic data

In this paper, we address a regression problem for economic data forecasting by using multiple-kernel learning (MKL) and propose a novel two-step multiple-kernel regression (MKR) method. The proposed MKR method firstly reformulates learning from linear convex combination of the basis kernels as a maximum eigenvalue problem. The optimal weights of basis kernels in the combination can be conveniently derived from solving the maximum eigenvalue problem by eigenvalue decomposition instead of solving complicated optimization like most existing MKR algorithms. By means of SVR optimization routine, finally, we can learn from basis kernels which have different predictive ability so as to improve prediction performance. More significantly, the way to address MKR problem can make sense of the weights and the correspondingly optimal kernel in terms of interpretability. To evaluate performance, the proposed MKR method is compared with the state-of-the-art methods on three real sets of economics data. The experimental results prove that the proposed two-step MKR method outperforms the other methods in terms of prediction performance and model selection, and demonstrates satisfied efficiency.