MULTIVARIATE CALIBRATION WITH SUPPORT VECTOR REGRESSION BASED ON RANDOM PROJECTION

Abstract

A multivariate calibration method with support vector regression based on random projection is proposed. The proposed algorithm reduces the dimensionality of high-dimensional spectral data by random projection, and then performs quantitative calibration using support vector regression. Support vector regression method can handle nonlinear regression which is usually encountered in spectral analysis. Moreover, model calculation and optimization on the projected data are relatively fast. A comparative study of the proposed method and partial least squares on four spectral datasets is presented. The proposed method allows drastic reduction in data size and computing time, while preserving the predict performance.