Canonical partial least squares-a unified PLS approach to classification and regression problems
We propose a new data compression method for estimating optimal latent variables in multi-variate classification and regression problems where more than one response variable is available. The latent variables are found according to a common innovative principle combining PLS methodology and canonical correlation analysis (CCA). The suggested method is able to extract predictive information for the latent variables more effectively than ordinary PLS approaches. Only simple modifications of existing PLS and PPLS algorithms are required to adopt the proposed method. Copyright (C) 2009 John Wiley & Sons, Ltd.
canonical correlation analysis partial least squares regression with several responses discriminant analysis powered partial least squares