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...

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the NonMetric NIPALS, the Non-Metric PLS Regres...

Orthonormalized partial least squares (OPLS) is often used to find a low-rank mapping between inputs X and outputs Y by estimating loading matrices A and B. In this paper, we introduce sparse orthonormalized PLS as an extension of conventional PLS that finds sparse estimates of A through the use of the elastic net algorithm. We apply sparse OPLS to the reconstruction of presented images from BO...

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...

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...

This research examines the time management strategies of individuals and gathers information on the complex temporal structures they experience and manage. Its focus is on understanding the relationship between the quality of individual time management and an individual’s understanding and use of temporal structures involving electronic calendar systems. This work consists of a survey study whi...

Approaches for meaningful regressor construction in the linear prediction problem are investigated in a framework similar to partial least squares and continuum regression, but weighted to allow for intelligent specification of an evaluative scheme. A cross-validatory continuum regression procedure is proposed, and shown to compare well with ordinary continuum regression in empirical demonstrat...

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