Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functio...

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partial least square and principal component regression methods were applied to various mixtures of Allura Red and Brilliant Blue to determine the concentrations. Colorants, at the same time, were analyzed with UV-spectrophotometry in chemical separation. The obtained experimental data have been evaluated by chemometric methods as Partial Least Squares (PLS) and Principle Component Regressi...

This paper addresses the use of temperature measurements to estimate product compositions in distillation columns. A simple linear multivariate calibration procedure based on steady-state data is used, which requires minimal modeling effort. It is found that these principal-component-regression (PCR) and partial-least-squares (PLS) estimators perform well, even for multicomponent mixtures, pres...

Partial least squares (pls) regression (a.k.a projection on latent structures) is a recent technique that combines features from and generalizes principal component analysis (pca) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal ...

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for ne...

This work provides a novel derivation based on optimization for the partial least squares (PLS) algorithm for linear regression and the kernel partial least squares (K-PLS) algorithm for nonlinear regression. This derivation makes the PLS algorithm, popularly and successfully used for chemometrics applications, more accessible to machine learning researchers. The work introduces Direct K-PLS, a...

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as...

In order to improve fitting and forecasting precision and solve the problem that some data with less sensitivity lead to low simulation precision of partial leastsquares regression (PLS for short) model, the new method of simulating freezing depth is presented according to ground temperature of different depths, air temperatures, surface temperatures and the like. Firstly, the PLS model, which ...

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...