Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on inputX, while principal component analysis (PCA) produces unsupervised decomposition of inputX. In this paper, the effect of outputYon theX-space decomposition in PLS is analyzed andgeometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric w...

Post-processing of PIV (particle image velocimetry) data typically contains three following stages: validation of the raw data, replacement of spurious and missing vectors, and some smoothing. A robust post-processing technique that carries out these steps simultaneously is proposed. The new all-in-one method (DCT-PLS), based on a penalized least squares approach (PLS), combines the use of the ...

Phospholipid scrambling (PLS) is a ubiquitous cellular mechanism involving the regulated bidirectional transport of phospholipids down their concentration gradient between membrane leaflets. ANO6/TMEM16F has been shown to be essential for Ca(2+)-dependent PLS, but controversy surrounds whether ANO6 is a phospholipid scramblase or an ion channel like other ANO/TMEM16 family members. Combining pa...

PLS Path Modeling (PLS-PM) is classically regarded as a component-based approach to Structural Equation Models and has been more recently revisited as a general framework for multiple table analysis. Here we propose two new modes for estimating outer weights in PLS-PM: the PLScore Mode and the PLScow Mode. Both modes involve integrating a PLS Regression as an estimation technique within the out...

OBJECTIVE To define the syndrome of primary lateral sclerosis (PLS) and disorders that contain features of both ALS and PLS, to determine the time beyond which PLS is less likely to become ALS clinically, and to determine the outcome of people with PLS and those who develop lower motor neuron (LMN) signs. METHODS The authors reviewed the records of all 39 patients initially diagnosed with PLS...

Over the past 15 years, the use of Partial Least Squares (PLS) in academic research has enjoyed increasing popularity in many social sciences including Information Systems, marketing, and organizational behavior. PLS can be considered an alternative to covariance-based SEM and has greater flexibility in handling various modeling problems in situations where it is difficult to meet the hard assu...

Dimension reduction is important during the analysis of gene expression microarray data, because the high dimensionality of data sets hurts the generalization performance of classifiers. Partial Least Squares (PLS) based dimension reduction is a frequently used method, since it is specialized in handling high dimensional data set and leads to satisfying classification performance. This paper in...

PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods. In addition, a simple procedure to choose the number of components is suggested. The connection between PLS dimension reduction and gene selection is examined and a ...

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...