Nonlinear Partial Least Squares An Overview

@inproceedings{Rosipal2011NonlinearPL,
  title={Nonlinear Partial Least Squares An Overview},
  author={Roman Rosipal},
  year={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… 

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References

SHOWING 1-10 OF 83 REFERENCES

Nonlinear Partial Least Squares Modeling of Phenyl Alkylamines with the Monoamine Oxidase Inhibitory Activities

This paper has carried out the QSAR analysis of four monoamine oxidase (MAO) inhibitory activities using the nonlinear PLS method, and the structural requirements could be estimated and the utility of the non linear P LS method was demonstrated.

A nonlinear PLS model

Overview and Recent Advances in Partial Least Squares

Partial Least Squares is a wide class of methods for modeling relations between sets of observed variables by means of latent variables as well as dimension reduction techniques and modeling tools.

Partial Least Squares Regression

PLSR—or PLSR1—is a regression method for collinear data, and can be seen as a competitor to principal component regression.

An overview on the shrinkage properties of partial least squares regression

It is pointed out that experiments on simulated and real world data show that bounding the absolute value of the PLS shrinkage factors by 1 seems to leads to a lower mean squared error.

Nonlinear PLS Modeling Using Neural Networks

Partial least squares for discrimination

Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial
...