Abstract
We propose, in the framework of the Symbolic Data Analysis (Diday 89), a new method which takes as input an array of objects described by interval data (Cazes & al. 97). In the factorial space, objects are described by an interval type principal components. It is therefore, represented in the factorial plane by a rectangle, a segment or a point. The interpretation parameters are generalised accordingly. An additional iterative subroutine is proposed to give a rectangular representation which takes into account the relative contributions of the objects. An application on “face’s recognition” is provided to illustrate the effectiveness of the proposed whole method.
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References
Cazes, P., Chouakria, A., Diday, E, Schecktman, Y. (1997). Extension de l’analyse en composantes principales à des données de type intervalle, in: Rev. Statistique. Appliquée, XLV, 5–24.
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© 1998 Springer-Verlag Berlin •Heidelberg
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Chouakria, A., Diday, E., Cazes, P. (1998). Vertices Principal Components Analysis With an Improved Factorial Representation. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_54
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DOI: https://doi.org/10.1007/978-3-642-72253-0_54
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