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.
978-88-8305-060-2
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.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11574/36463
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact