Among linear dimensional reduction techniques. Principal Component Analysis (PCA) presents many optimal properties. Unfortunately, in many applicative case PCA doesn't produce full interpretable results. For this reason, several authors proposed methods able to produce sub optimal components but easier to interpret like Simple Component Analysis (Rousson and Gasser, (2004)). Following Rousson and Gasser, in this paper we propose to modify the algorithm used for the Simple Component Analysis by introducing the RV coefficients (SCA-RV) in order to improve the interpretation of the results.

Simple component analysis based on RV coefficient

GALLO, Michele;
2006-01-01

Abstract

Among linear dimensional reduction techniques. Principal Component Analysis (PCA) presents many optimal properties. Unfortunately, in many applicative case PCA doesn't produce full interpretable results. For this reason, several authors proposed methods able to produce sub optimal components but easier to interpret like Simple Component Analysis (Rousson and Gasser, (2004)). Following Rousson and Gasser, in this paper we propose to modify the algorithm used for the Simple Component Analysis by introducing the RV coefficients (SCA-RV) in order to improve the interpretation of the results.
2006
3-540-35977-X
File in questo prodotto:
File Dimensione Formato  
DAC2005.pdf

solo utenti autorizzati

Tipologia: Documento in Post-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 1.72 MB
Formato Adobe PDF
1.72 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/11574/32074
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact