Results 251 to 260 of about 232,548 (306)

An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging. [PDF]

Sensors (Basel)
Adegbenjo AO, Liu L, Ngadi MO.
europepmc   +1 more source

Evaluation of Freezing-Induced Changes in Aroma Profiles of Pomegranate Juice by Quantitative Descriptive Sensory Analysis, Gas Chromatography-Mass Spectrometry/Olfactometry, Odor Activity Values, Orthogonal Partial Least Squares-Discriminant Analysis, and Odorant Addition Experiment. [PDF]

Foods
Chen Y, Wang P, He W, Tian H, Geng J, Zhang R, Zhan P.   +6 more
europepmc   +1 more source

Brain activations elicited during task-switching generalize beyond the task: A partial least squares correlation approach to combine fMRI signals and cognition. [PDF]

Hum Brain Mapp
Skolasinska P, Qin S, Voss M, Lee HK, Basak C.   +4 more
europepmc   +1 more source

Dignity, Resilience, and Quality of Life in Patients With Cardiac Disease: A Partial Least Squares Structural Equation Modeling Approach. [PDF]

J Cardiovasc Nurs
Huang CL, Hsu CH, Hsu SF, Tung HH.
europepmc   +1 more source

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Analytical Solution to Partial Least Squares

Information Sciences, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhijiang Lou, Shan Lu 0009, Youqing Wang, Xin Ma   +3 more
openaire   +1 more source

Boosting Partial Least Squares

Analytical Chemistry, 2005
A difficulty when applying partial least squares (PLS) in multivariate calibration is that overfitting may occur. This study proposes a novel approach by combining PLS and boosting. The latter is said to be resistant to overfitting. The proposed method, called boosting PLS (BPLS), combines a set of shrunken PLS models, each with only one PLS component.
Zhang, Menghui, Xu, Qing, Massart, Desire   +2 more
openaire   +3 more sources

Partial Least Squares

Long Range Planning, 2012
Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire   +2 more sources

Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

2012
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
openaire   +2 more sources

Robust Partially-Compressed Least-Squares

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off.
Stephen Becker, Ban Kawas, Marek Petrik
openaire   +1 more source

Approximate kernel partial least squares

Annals of Mathematics and Artificial Intelligence, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiling Liu, Shuisheng Zhou
openaire   +1 more source