Results 161 to 170 of about 614,693 (221)

Orthogonal Nonlinear Partial Least-Squares Regression

Industrial & Engineering Chemistry Research, 2003
A multivariate statistical regression technique is proposed to address underlying nonlinear correlations among the predictor variables, as well as between the predictor variables and the response variable. The method is based on a neural network architecture that preserves the orthogonality properties of the principal component analysis (PCA) approach.
Fuat Doymaz, Ahmet Palazoglu, Jose A. Romagnoli   +2 more
openaire   +1 more source

Solving Partial Least Squares Regression via Manifold Optimization Approaches

IEEE Transactions on Neural Networks and Learning Systems, 2019
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two data sets. However, all existing approaches often optimize a PLSR model in Euclidean space and take a successive strategy to calculate all
Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin   +4 more
semanticscholar   +1 more source

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system.

The Journal of the Science of Food and Agriculture, 2019
BACKGROUND Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-
Lianbo Guo, Yunxin Yu, Hanyu Yu, Yun Tang, Jun Li, Yu Du, Yanwu Chu, Shixiang Ma, Yuyang Ma, Xiaoyan Zeng   +9 more
semanticscholar   +1 more source

The objective function of partial least squares regression

Journal of Chemometrics, 1998
A simple objective function in terms of undeflated X is derived for the latent variables of multivariate PLS regression. The objective function fits into the basic framework put forward by Burnham et al. (J. Chemometrics, 10, 31–45 (1996)). We show that PLS and SIMPLS differ in the constraint put on the length of the X-weight vector.
ter Braak, C.J.F., de Jong, S.
openaire   +2 more sources

On a partial least squares regression model for asymmetric data with a chemical application in mining

Chemometrics and Intelligent Laboratory Systems, 2019
In chemometrical applications, covariates in regression models are often correlated, causing a collinearity problem that can be solved by partial least squares (PLS) regression.
M. Huerta, V. Leiva, Shuangzhe Liu, Marcelo Rodríguez, Danny Villegas   +4 more
semanticscholar   +1 more source

Extreme partial least-squares regression

2021
A new approach, called Extreme-PLS, is proposed for dimension reduction in regression and adapted to distribution tails. The goal is to find linear combinations of predictors that best explain the extreme values of the response variable by maximizing the associated covariance.
Bousebata, Meryem, Enjolras, Geoffroy, Girard, Stéphane   +2 more
openaire   +2 more sources

Sparse Kernel Partial Least Squares Regression

2003
Partial Least Squares Regression (PLS) and its kernel version (KPLS) have become competitive regression approaches. KPLS performs as well as or better than support vector regression (SVR) for moderately-sized problems with the advantages of simple implementation, less training cost, and easier tuning of parameters.
Michinari Momma, Kristin P. Bennett
openaire   +1 more source

Extension and significance testing of Variable Importance in Projection (VIP) indices in Partial Least Squares regression and Principal Components Analysis

Chemometrics and Intelligent Laboratory Systems, 2023
Benjamin Mahieu, E. Qannari, Benoît Jaillais   +2 more
semanticscholar   +1 more source

Robust methods for partial least squares regression

Journal of Chemometrics, 2003
AbstractPartial least squares regression (PLSR) is a linear regression technique developed to deal with high‐dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm, this being the leading PLSR algorithm because of its speed and efficiency.
M. Hubert, K. Vanden Branden
openaire   +1 more source

Additive Splines for Partial Least Squares Regression

Journal of the American Statistical Association, 1997
Abstract This article introduces a generalization of the partial least squares regression (PLS). Transforming the predictors by means of spline functions is a useful way to extend PLS into nonlinearity and to obtain a multiresponse additive model. We describe both statistical and computational aspects of this new method, termed additive splines partial
Jean-François Durand, Robert Sabatier
openaire   +1 more source