Abstract
Any data table produced in a chemical investigation can be analysed by bilinear projection methods, i. e. principal components and factor analysis and their extensions. Representing the table rows (objects) as points in a p-dimensional space, these methods project the point swarm of the data set or parts of it down on a F-dimensional subspace (plane or hyperplane). Different questions put to the data table correspond to different projections.
This provides an efficient way to convert a data table to a few informative pictures showing the relations between objects (table rows) and variables (table columns).
The methods are presented geometrically and mathematically in parallell with chemical illustrations. more dangerous in the long run than methods that are conservative with respect to the amount of extracted information.
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Wold, S. et al. (1984). Multivariate Data Analysis in Chemistry. In: Kowalski, B.R. (eds) Chemometrics. NATO ASI Series, vol 138. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1026-8_2
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DOI: https://doi.org/10.1007/978-94-017-1026-8_2
Publisher Name: Springer, Dordrecht
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