Multi-input-multi-output support vector machine based on principal curve

Abstract

To solve the problem that the traditional Multi-Input-Multi-Output(MIMO) Support Vector Machine(SVM) generally ignore the dependency among all outputs,a new MIMO SVM algorithm based on principal curve was proposed in this paper.Following the assumption that the model parameters of all outputs locate on a manifold,this paper firstly constructed a manifold regularization based on the Multi-dimensional Support Vector Regression(M-SVR),where the regularizer was the squared distance from the output parameters to the principal curve through the middle of all parameters' set.Secondly,considering the non-convexity of this regularization,this paper introduced an alternative optimization method to calculate the model parameters and principal curve in turn until convergence.The experiments on simulated data and real-life dynamic load identification data were conducted,and the results show that the proposed algorithm performs better than M-SVR and SVM based separate modeling method in terms of prediction precision and numerical stability.