Number of latents The same number of factors will be extracted for PLS responses as for PLS factors. The researcher must specify how many latents to extract (in SPSS the default is 5). There is no one criterion for deciding how many latents to employ. Common alternatives are: 1. Cross-validating the model with increasing numbers of factors, then choosing the number with minimum prediction error...

Extracting facial feature points such as eyes, mouth and nose plays an important role in many applications. Most of the proposed methods are base on the geometrical features of images. In this paper, a novel method based on Partial Least Square Regression (PLSR) model is introduced to extract the relationship between the feature point coordinates and gray value distribution in the image. The pr...

Some of the models using partial least squares (PLS) in Information Systems (IS) field may have serious problems because do not properly address endogeneity. This may suppose a problem in IS theory building because it may lead IS scholars to non-correct results. Although the IS community’s awareness is rising, we do not have a clear understanding of the problem nor fine-grained practical guidel...

Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years. In this letter, we propose a Bayesian formulation of PLS along with s...

Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphas...

Pls regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., predictors). It originated in the social sciences (specifically economy, Herman Wold 1966) but became popular first in chemomet...