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...

Partial least squares (PLS) methods have recently been used for many pattern recognition problems in computer vision. Here, PLS is primarily used as a supervised dimensionality reduction tool to obtain effective feature combinations for better learning. However, application of PLS to large datasets is hindered by its higher computational cost. We propose an approach to accelerate the classical ...

PLS Generalised Linear Regression: foundations and recent advances with variable selection and validation procedures Vincenzo Esposito Vinzi Universita degli Studi di Napoli; [email protected] PLS (Partial Least Squares) univariate regression (PLS1) is a model linking a numerical dependent variable to a set of numerical (or dummy) explanatory variables especially feasible in situa...

BACKGROUND Loss-of-function point mutations in the cathepsin C gene are the underlying genetic event in patients with Papillon-Lefèvre syndrome (PLS). PLS neutrophils lack serine protease activity essential for cathelicidin LL-37 generation from hCAP18 precursor. AIM We hypothesized that a local deficiency of LL-37 in the infected periodontium is mainly responsible for one of the clinical hal...

Near-infrared (NIR) spectrometry will present a more promising tool for quantitative measurement if the robustness and predictive ability of the partial least square (PLS) model are improved. In order to achieve the purpose, we present a new algorithm for simultaneous wavelength selection and outlier detection; at the same time, the problems of background and noise in multivariate calibration a...

Partial least squares (PLS) is sometimes used as an alternative to covariance-based structural equation modeling (SEM). This paper briefly reviews currently available SEM techniques, and provides a critique of the perceived advantages of PLS over covariance-based SEM as commonly cited by PLS users. Specific attention is drawn to the primary disadvantage of PLS, namely the lack of consistency of...

Using solid state radioimmunoassays developed by the first author, changes in the urine level of plasmin-like substances (PLS) and fibrin degradation products (FDP) before and after human kidney transplantation were determined in 49 transplant patients. Averages of urine PLS and FDP in a normal population of 51 persons were 0.13+/-0.10 (SD) and 0.14+/-0.07 microng/ml, respectively. In all trans...

BACKGROUND Motor neuron diseases can affect the upper motor neuron and/or the lower motor neuron. Both amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS) are motor neuron diseases, and there is much debate as to whether these are 2 separate disorders or simply 2 points on a continuum. OBJECTIVE To determine which clinical features at onset and during follow-up could help ...

Neuronal cells are susceptible to many stresses, which will cause the apoptosis and neurodegenerative diseases. The precise molecular mechanism behind the neuronal protection against these apoptotic stimuli is necessary for drug discovery. In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the ...

4 We build connections between envelopes, a recently proposed context for efficient estima5 tion in multivariate statistics, and multivariate partial least squares (PLS) regression. In partic6 ular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which 7 opens the door to pursuing the same goals as PLS but using different envelope estimators. It 8 is argued that...