Mixture of partial least squares experts and application in prediction settings with multiple operating modes

Abstract

This paper addresses the problem of online quality prediction in processes with multiple operating modes. The paper proposes a new method called mixture of partial least squares regression (Mix-PLS), where the solution of the mixture of experts regression is performed using the partial least squares (PLS) algorithm. The PLS is used to tune the model experts and the gate parameters. The solution of Mix-PLS is achieved using the expectation–maximization (EM) algorithm, and at each iteration of the EM algorithm the number of latent variables of the PLS for the gate and experts are determined using the Bayesian information criterion. The proposed method shows to be less prone to overfitting with respect to the number of mixture models, when compared to the standard mixture of linear regression experts (MLRE). The Mix-PLS was successfully applied on three real prediction problems. The results were compared with five other regression algorithms. In all the experiments, the proposed method always exhibits the best prediction performance.