Interval data proved to be a useful coding in big data applications since many individuals can be collapsed into a single unit, so that each cell of the table contains a bounded interval. This contribution presents the Principal Component Analysis (PCA) for interval-valued data based on midpoints and radii approach, where the symbolic standardization is used, along with an ad-hoc Procrustean rotation.
Schisa, V., Iodice D'Enza, A., Palumbo, F. (2022). Dimensionality reduction and visualization for interval-valued data via midpoints-ranges principal component analysis.
Dimensionality reduction and visualization for interval-valued data via midpoints-ranges principal component analysis
Viviana SchisaInvestigation
;
2022
Abstract
Interval data proved to be a useful coding in big data applications since many individuals can be collapsed into a single unit, so that each cell of the table contains a bounded interval. This contribution presents the Principal Component Analysis (PCA) for interval-valued data based on midpoints and radii approach, where the symbolic standardization is used, along with an ad-hoc Procrustean rotation.File in questo prodotto:
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