Error-based nonlinear partial least squares method embedded least squares support vector machine and its application to qantitative structure-activity relationships

A new nonlinear partial least squares algorithm embedded least squares support vector machine (LSSVM) into the regression framework of partial least squares(PLS) method was proposed. In this approach, LSSVM was used to fit the nonlinear inner relations between PLS components, thus a multi-input multi-output nonlinear modeling task was decomposed into linear outer relations and simple nonlinear inner relations that were performed by a number of single-input single-output LSSVM models. By using the universal approximation property of LSSVM, the PLS modeling method was generalized to a non-linear framework. Subsequently, to increase PLS components interpretative capability, the error-based weights updating procedure in the PLS input outer model was deduced and implemented in the LSSVM-PLS regression framework. Finally, the EB-LSSVM-PLS was applied to quantitative structure-activity relationships modeling of flavanone compound. Compared with the other three approach partialeast squares regression (PLSR), EB-neural network (NN) PLS and LSSVM, the EB-LSSVM-PLS approach has better prediction performance and stability.