Premium
Partial least squares algorithms and methods
Author(s) -
Esposito Vinzi Vincenzo,
Russolillo Giorgio
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1239
Subject(s) - partial least squares regression , multivariate statistics , computer science , exploratory data analysis , principal component analysis , principal component regression , path analysis (statistics) , algorithm , regression analysis , least squares function approximation , data set , data mining , machine learning , artificial intelligence , statistics , mathematics , estimator
Partial least squares (PLS) refers to a set of iterative algorithms based on least squares that implement a broad spectrum of both explanatory and exploratory multivariate techniques, from regression to path modeling, and from principal component to multi‐block data analysis. This article focuses on PLS regression and PLS path modeling, which are PLS approaches to regularized regression and to predictive path modeling. The computational flows and the optimization criteria of these methods are reviewed in detail, as well as the tools for the assessment and interpretation of PLS models. The most recent developments and some of the most promising on going researches are enhanced. WIREs Comput Stat 2013, 5:1–19. doi: 10.1002/wics.1239 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical Models > Linear Models Algorithms and Computational Methods > Least Squares