Multivariate methods for the analysis of data-bases, process monitoring, and control in the material processing industries

This paper gives an overview of multivariate methods for extracting information from large process databases. Process data matrices are almost always of less than full statistical rank, and therefore latent variable methods are shown to be well suited to obtaining useful subspace models for treating a variety of important industrial problems. An overview of the important concepts behind latent variable models is presented and the methods are …