Codice01/2011
TitoloDimensional Reduction of Functional Data by means of Principal Differential Analysis
Data2011-01-05
Autore/iDalla Rosa, M.; Sangalli, L. M.; Vantini, S.
LinkDownload full text
PubblicatoMatilde Dalla Rosa, Laura M. Sangalli and Simone Vantini (2014), Principal Differential Analysis of the Aneurisk65 Data Set. Advances in Data Analysis and Classification, Vol. 8, Issue 3, pp. 287-302.
AbstractWe explore the use of principal differential analysis (PDA) as a tool for performing dimensional reduction of functional data sets. In particular, we compare the results provided by PDA and by functional principal component analysis (FPCA) in the dimensional reduction of three synthetic data sets, and of a real data set concerning 65 vascular geometries (i.e., the AneuRisk data set). The analyses of the synthetic data sets show that PDA can provide an alternative and effective representation of functional data that is always easily interpretable in terms of constant, exponential, sinusoidal, or dampedsinusoidal functions and not affected by the presence of clusters or strong correlations among the original components. Moreover, in the analysis of the AneuRisk data set, PDA is able to detect important features of the data that FPCA is not able to detect.