, cL), Chemometrics (Geladi and Kowalski, 1986), producing stable prediction models even when the number of samples is considerably smaller than the dimensionality of the feature space.
pLSR fits a smaller number of latent variables (10 − 100 in practice) such that the correlation between the feature vectors for pivots in the two domains are maximised in this latent space.