Abstract
In contrast with the traditional Principal Components framework we look at Factor Analysis as a modelization technique to explain the variability within a data matrix by means of simple structures, i.e. by matrices of rank one. This viewpoint makes the following essential elements of this methodology apparent: (i) a reference matrix X 0 with respect to which deviations are to be measured, and (ii) a matrix norm ‖.‖Λ,Γ defining the measure of the total variation.
This formalization is simple and, at the same time, it allows for extensions of the technique. Finally this presentation makes explicit the choices that one has to face among possible variations, and clarifies some specific properties of Correspondence Analysis in relation with these choices.
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© 1994 Springer Science+Business Media Dordrecht
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Lejeune, M. (1994). A Generic Look at Factor Analysis. In: Caliński, T., Kala, R. (eds) Proceedings of the International Conference on Linear Statistical Inference LINSTAT ’93. Mathematics and Its Applications, vol 306. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1004-4_30
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DOI: https://doi.org/10.1007/978-94-011-1004-4_30
Publisher Name: Springer, Dordrecht
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