Rosipal R., Trejo L.J.
Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, Lodhi H., Yamanishi Y. (eds.), ACCM, IGI Global, pp. 169-189, 2011.
In many areas of research and industrial situations, including many data analytic problems in chemistry, a strong nonlinear relation between different sets of data may exist. While linear models may be a good simple approximation to these problems, when nonlinearity is severe they often perform unacceptably. The nonlinear partial least squares (PLS) method was developed in the area of chemical data analysis. A specific feature of PLS is that relations between sets of observed variables are modeled by means of latent variables usually not directly observed and measured. Since its introduction, two methodologically different concepts of fitting existing nonlinear relationships initiated development of a series of different nonlinear PLS models. General principles of the two concepts and representative models are reviewed in this chapter. The aim of the chapter is two-fold i) to clearly summarize achieved results and thus ii) to motivate development of new computationally efficient nonlinear PLS models with better performance and good interpretability.