Improved modelling for low-correlated multiple responses by common-subset-of-independent-variables partial-least-squares

In this study, a new approach for PLS modelling for low-correlated multiple responses, called Common-Subset-of-Independent-Variables Partial-Least-Squares, denoted as CSIV-PLS1, is proposed and evaluated. In CSIV-PLS1, for each response vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG.