Univariate partial least squares (PLS) is a method of modeling relationships between a Y variable and other explanatory vanables. It may be used with any number of explanatory variables, even far more than the number of observations. A simple interpretation is given that shows the method to be a straightforward and reasonable way of forming prediction equations. Its relationship to multivariate...

Partial least squares (PLS) estimation of path models has become very popular in IS research, as an alternative to covariance-based methods. PLS path modeling is often referred to as being useful for “predictive” applications. In this work, we investigate the predictive aspects of PLS path modeling and its relation to predictive analytics and predictive assessment. In particular, we compare it ...

Partial least squares (PLS) regression is a powerful and frequently applied technique in multivariate statistical process control when the process variables are highly correlated. Selection of the number of latent variables to build a representative model is an important issue. A metric frequently used by chemometricians for the determination of the number of latent variables is that of Wold’s ...

Partial least squares is a popular method for soft modelling in industrial applications. This paper introduces the basic concepts and illustrates them with a chemometric example. An appendix describes the experimental PLS procedure of SAS/STAT software.

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variab...

We present a method for computing partial spectra of Hermitian matrices, based on a combination of subspace iteration with rational filtering. In contrast with classical rational filters derived from Cauchy integrals or from uniform approximations to a step function, we adopt a least-squares (LS) viewpoint for designing filters. One of the goals of the proposed approach is to build a filter tha...

In the machine learning field, feature selection is used to discard the redundant information and improve the learning accuracy. In this paper, the redundant information is reused in the learning of partial least squares method within the frame of multitask learning. This newly proposed method is used to solve the multivariate calibration problem, a classic problem in the analytical chemistry f...

The main contributions of this paper can be summarized as follows. First, we compare the Partial Least Squares (PLS) and the Principal Component Analysis (PCA), under fairly general conditions. (In particular, the existence of a true linear regression is not assumed.) We prove that PLS and PCA are equivalent, to within a rst-order approximation, hence providing a theoretical explanation for emp...

In this work we find out how PLS algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had until now been totally unexplored, allowed us to devise a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. Mots-clès: Analyse des données data mining, Problèmes inverses et sparsité

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (a) can be truly characterized as a technique for structural equation modeling (SEM), (b) is able to correct for measur...