Automatic configuration of optimized sample-weighted least-squares support vector machine by particle swarm optimization for multivariate spectral analysis

Abstract

Due to the high dimensionality and complexity of multivariate spectral data space and the uncertainty involved in the sampling process, the representation of training samples in the whole sample space is difficult to evaluate and selection of representative training samples for conventional multivariate calibration depends largely on experiential methods. If the training samples fail to represent the sample space, sometimes the prediction of new samples can be degraded. To circumvent this problem, in this paper, a new optimized sample-weighted least-squares support vector machine (OSWLS-SVM) multivariate calibration method is proposed by incorporating the concept of weighted sampling into LS-SVM, where the complexity and predictivity of the model are considered simultaneously. A recently suggested global optimization technique base on particle swarm optimization (PSO) is invoked to simultaneously search for the best sample weights and the hyper-parameters involved in OSWLS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. The implementation of PSO achieves complete automatization of the OSWLS-SVM modeling process and high efficiency in convergence to a desired optimum. Three real multivariate spectral data sets including two public data sets and an experimental data set are investigated and the results are compared favorably with those of PLS and LS-SVM to demonstrate the advantages of the proposed method. The stability and efficiency of OSWLS-SVM is also surveyed, the results reveal that the proposed method can obtain desirable results within moderate PSO cycles.