منابع مشابه
Partial Least Square Regression PLS-Regression
PLS regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. Its goal is to predict or analyze 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 factors called latent variables which have the best predictive pow...
متن کاملPartial least squares methods: partial least squares correlation and partial least square regression.
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. When the goal is to find the shared information between two tables, the ap...
متن کاملPartial least square regression applied to the QTLMAS 2010 dataset
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...
متن کاملApplication of Partial Least Square Regression in Uncertainty Analysis
The aim of this work is to show how partial least squares (PLS) regression when combined with two other techniques Karhunen-Loeve (KL) expansion and Markov chain Monte Carlo (MCMC) can be efficient and effective at addressing parameter uncertainties that affect the predictive ability of a model for critical applications such as monitoring and control. We introduce a combination of PLS regressio...
متن کاملFacial Feature Point Extraction by Partial Least Square Regression
Extracting facial feature points such as eyes, mouth and nose plays an important role in many applications. Most of the proposed methods are base on the geometrical features of images. In this paper, a novel method based on Partial Least Square Regression (PLSR) model is introduced to extract the relationship between the feature point coordinates and gray value distribution in the image. The pr...
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ژورنال
عنوان ژورنال: Journal of Chemometrics
سال: 2017
ISSN: 0886-9383
DOI: 10.1002/cem.2964