Discriminant Partial Least Squares for Compositional data (DPLS-CO) was recently proposed by Gallo (2008). The aim of this paper is to show that DPLS-CO is a better dimensionality reduction technique than the LogContrats Principal Component Analysis (LCPCA) for dimensional reduction aimed at discrimination when a compositional training dataset is available.
Discriminant Partial Least Square on Compositional Data: a Comparison with the Log-Contrast Principal Component Analysis
GALLO, Michele;
2008-01-01
Abstract
Discriminant Partial Least Squares for Compositional data (DPLS-CO) was recently proposed by Gallo (2008). The aim of this paper is to show that DPLS-CO is a better dimensionality reduction technique than the LogContrats Principal Component Analysis (LCPCA) for dimensional reduction aimed at discrimination when a compositional training dataset is available.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
GALLO_2008_p45.pdf
accesso aperto
Tipologia:
Documento in Post-print
Licenza:
PUBBLICO - Pubblico senza Copyright
Dimensione
207.9 kB
Formato
Adobe PDF
|
207.9 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
