Treffer: DPLS and PPLS: two PLS algorithms for large data sets
Title:
DPLS and PPLS: two PLS algorithms for large data sets
Authors:
Source:
Partial least squaresComputational statistics & data analysis. 48(1):125-138
Publisher Information:
Amsterdam: Elsevier Science, 2005.
Publication Year:
2005
Physical Description:
print, 27 ref
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Analyse multivariable, Multivariate analysis, Inférence linéaire, régression, Linear inference, regression, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Analyse donnée, Data analysis, Análisis datos, Architecture ordinateur, Computer architecture, Arquitectura ordenador, Calcul statistique, Statistical computation, Cálculo estadístico, Régression statistique, Statistical regression, Regresión estadística, Vecteur propre, Eigenvector, Vector propio, 62Jxx, Algorithme PLS, PLS algorithm
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Departamento de Informática, Pontificia Universidade Católica do Rio de Janeiro, Rua Marqués de São Vicente 225, Gávea, Rio de Janeiro, Brazil
ISSN:
0167-9473
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.16461610
Database:
PASCAL Archive
Weitere Informationen
Two enhancements to the PLS regression algorithm are presented. The first, direct PLS (DPLS), offers a direct approximate formulation for the calculation of the required eigenvectors when dealing with more than one dependent variable. The second enhancement is parallel PLS (PPLS), a parallel version of the PLS algorithm restricted to the case of only one dependent variable for the regression model. In the experiments, DPLS shows a 40% faster running time, while the PPLS produces a speedup of 3 for the first four machines in a computer cluster architecture.