Year-end Sale % OFF 
Abstract
The probabilistic PLS (PPLS) algorithm derives the latent variables by maximizing the likelihood of input scores and quality scores, but imposes no constraint on the input residuals and the quality residuals, which implies that residuals may contain large information. Motivated by the concurrent PLS method, this paper proposes a concurrent PPLS (CPPLS) method to perform further decomposition of these residuals, and then two more subspaces are obtained. In this method, the maximum-likelihood method along with the expectation-maximization (EM) algorithm are employed to develop the model, in which the variance of each variable explained by latent variables is introduced to determine the number of latent variables. Based on the CPPLS model, five monitoring statistics all based on Mahalanobis norm are constructed for the evaluation of five subspaces decomposed by CPPLS, respectively.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
