Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets. However, most of algorithm implementations of PLSR may only achieve a suboptimal solution through an optimization on the Euclidean space. In this paper, we propose several novel PLSR models on Riemannian manifolds and develop optimization algorithms based on Riemannian g...

Partial Least Squares Regression (PLSR) can integrate a great number of variables and overcome collinearity problems, a fact that makes it suitable for intensive agronomical practices such as land application. In the present study a PLSR model was developed to predict important management goals, including biomass production and nutrient recovery (i.e., nitrogen and phosphorus), associated with ...

Ž . Ž PLS-regression PLSR is the PLS approach in its simplest, and in chemistry and technology, most used form two-block . predictive PLS . PLSR is a method for relating two data matrices, X and Y, by a linear multivariate model, but goes beyond traditional regression in that it models also the structure of X and Y. PLSR derives its usefulness from its ability to analyze data with many, noisy, ...

Opportunistic Data Forwarding (ODF) has drawn much attention in mobile adhoc networking research in recent years. The effectiveness of ODF in MANET depends on a suitable routing protocol which provides a powerful source routing services. PLSR is featured by source routing, loop free and small routing overhead. The update messages in PLSR are integrated into a tree structure and no need to time ...

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the ...

Generalised Procrustes analysis (GPA) is a method for producing a group average from rotated versions of a set of individual data matrices followed by bi-linear approximation of this group average for graphical inspection. Partial Least Squares Regression (PLSR) is a method for relating one data matrix to another data matrix, via bi-linear low-rank regression modelling. The merger of these meth...

BACKGROUND Decision making in patients with low flow-low gradient aortic stenosis mainly depends on the actual stenosis severity and left ventricular function, which is of prognostic importance. We used 2-dimensional strain parameters measured by speckle tracking at rest and during dobutamine stress echocardiography to document the extent of myocardial impairment, its relationship with hemodyna...

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen ...

BACKGROUND Partial least square regression (PLSR) was used to analyze the data of the QTLMAS 2010 workshop to identify genomic regions affecting either one of the two traits and to estimate breeding values. PLSR was appropriate for these data because it enabled to simultaneously fit several traits to the markers. RESULTS A preliminary analysis showed phenotypic and genetic correlations betwee...