Support vector regression in sum space for multivariate calibration

• An SVR algorithm in sum spaces is proposed for multivariate calibration.
• It represents the target function as the sum of single kernel decision functions.
• Each single kernel function can approximate different components of target function.
• Experimental results show that the proposed SVRSS outperforms state-of-the-art algorithms.

In this paper, a support vector regression algorithm in the sum of reproducing kernel Hilbert spaces (SVRSS) is proposed for multivariate calibration. In SVRSS, the target regression function is represented as the sum of several single kernel decision functions, where each single kernel function with specific scale can approximate certain component of the target function. For sum spaces with two Gaussian kernels, the proposed method is compared, in terms of RMSEP, to traditional chemometric PLS calibration methods and recent promising SVR, GPR and ELM methods on a simulated data set and four real spectroscopic data sets. Experimental results demonstrate that SVR methods outperform PLS methods for spectroscopy regression problems. Moreover, SVRSS method with multi-scale kernels improves the single kernel SVR method and shows superiority over GPR and ELM methods.