https://imedea.uib-csic.es/master/cambioglobal/Modulo_V_cod101615/Theory/lit_support/pca_wold.pdf

Principal component analysis

https://libpls.net/publication/PLS_basic_2001.pdf

PLS-regression: a basic tool of chemometrics

http://home.mit.bme.hu/~horvath/IDA/1-s2.0-0003267086800289-main.pdf

Partial least-squares regression: a tutorial

General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

Applied Predictive Modeling

Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlations among problem variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA identifies only linear correlations between variables, NLPCA uncovers both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. NLPCA operates by training a feedforward neural network to perform the identity mapping, where the network inputs are reproduced at the output layer. The network contains an internal “bottleneck” layer (containing fewer nodes than input or output layers), which forces the network to develop a compact representation of the input data, and two additional hidden layers. The NLPCA method is demonstrated using time-dependent, simulated batch reaction data. Results show that NLPCA successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.

Nonlinear principal component analysis using autoassociative neural networks

A new multivariate curve resolution method is presented and tested with data of various levels of complexity. Rotational and intensity ambiguities and the effect of selectivity on resolution are the focus. Analysis of simulated data provides the general guidelines concerning the conditions for uniqueness of a solution for a given problem. Multivariate curve resolution is extended to the analysis of three-way data matrices. The particular case of three-way data where only one of the orders is common between slices is studied in some detail

Bruce R. Kowalski

Papers

Partial least-squares regression: a tutorial

Selectivity, local rank, three‐way data analysis and ambiguity in multivariate curve resolution

An introduction to multivariate calibration and analysis

Multivariate instrument standardization

Theory of analytical chemistry