Orthogonal PLS (O-PLS) and related algorithms
The concept of orthogonalized partial least squares regression or, better, as it was originally named, orthogonalized projection to latent structures (O-PLS) was first introduced in 2001 by Johann Trygg and Svante Wold, as a way to deal with the large amount of variation in predictor matrices for multivariate calibration (and classification), not correlated to the responses. In this context, O-PLS operates by partitioning the systematic variance in the X block into a Y relevant and an orthogonal data sets, both having a bilinear structure.