Partial least squares for discrimination

  title={Partial least squares for discrimination},
  author={Matthew L. Barker and William S. Rayens},
  journal={Journal of Chemometrics},
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 empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphasize group structure? Using PLS in this manner has heurestic support owing to the relationship between… 

Partial least squares discrimination with heterogeneous covariance structures

Barker and Rayens 1 argued that partial least squares (PLS) is to be preferred over principal components analysis (PCA) when linear discrimination is the goal and dimension reduction is required as a

Powered partial least squares discriminant analysis

From the fundamental parts of PLS‐DA, Fisher's canonical discriminant analysis (FCDA) and Powered PLS (PPLS), we develop the concept of powered PLS for classification problems (PPLS‐DA). By taking

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Multiclass partial least squares discriminant analysis: Taking the right way—A critical tutorial

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PLS and dimension reduction for classification

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A robust partial least squares method with applications

Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the

A robust partial least squares regression method with applications

Partial least squares (PLS) regression is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the



Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least s

[Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, October 25th, 1989, Professor D. V. Hinkley in the Chair] SUMMARY The paper addresses the

A Statistical View of Some Chemometrics Regression Tools

Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering


We prove that the two algorithms given in the literature for partial least squares regression are equivalent, and use this equivalence to give an explicit formula for the resulting prediction

Further aspects of the theory of multiple regression

  • M. Bartlett
  • Mathematics
    Mathematical Proceedings of the Cambridge Philosophical Society
  • 1938
This paper may be regarded as a sequel to a previous papers(1) in these Proceedings. The vector and matrix notation of that paper used for a statistical sample is systematized somewhat further, so

PLS regression methods

In this paper we develop the mathematical and statistical structure of PLS regression. We show the PLS regression algorithm and how it can be interpreted in model building. The basic mathematical

Source contributions to ambient aerosol calculated by discriminat partial least squares regression (PLS)

Partial least squares regression (PLS) is proposed for solving ir pollution source apportionment problems as an alternative method to the frequently used chemical mass balance technique. A

Analysis of ageing and typification of vintage ports by partial least squares and soft independent modelling class analogy

Vintage port is a wine produced in very limited amounts in the Portuguese region of the Douro Valley. It ages very slowly in bottles and few analytical data are available. Thus, the composition of 24