Many different algorithms exist for PLSR, depending on the properties of the input data, that is, the dimensions of X and Y. The purpose of the more efficient algorithms was to estimate the ...

Second, PLSR can solve regression and classification problems simultaneously. Third, the PLSR algorithm is relatively robust to the number of latent variables. Then, PLSR enables later inferences of ...

We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a ...

In general, nonlinear regression algorithms (NN, KRR, and GPR) outperformed linear techniques (PCR and PLSR) in terms of accuracy, bias, and robustness. Most robust results along gradients of training ...