Abstract
This paper deals with a non-symmetrical analysis of two multiple data sets in order to study the structure of dependence among sets of variables which play different role in the analysis. This approach represents a generalization of the Constrained Principal Component Analysis (CPCA) (D’Ambra and Lauro, 1982).
this research has been supported with a grant C.N.R. 1998 (Prof. L. D’Ambra).
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Amenta, P., D’Ambra, L. (2001). Generalized Constrained Principal Component Analysis. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_17
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DOI: https://doi.org/10.1007/978-3-642-59471-7_17
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