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.
Titolo: | Discriminant Partial Least Square on Compositional Data: a Comparison with the Log-Contrast Principal Component Analysis |
Autori: | |
Data di pubblicazione: | 2008 |
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. |
Handle: | http://hdl.handle.net/11574/36463 |
ISBN: | 978-88-8305-060-2 |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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