Results 151 to 160 of about 614,693 (221)

The Comparison Of Partial Least Squares Regression, Principal Component Regression And Ridge Regression With Multiple Linear Regression For Predicting Pm10 Concentration Level Based On Meteorological Parameters

, 2021
Air pollution shows itself as a serious problem in big cities in Turkey, especially for winter seasons. Particulate atmospheric pollution in urban areas is considered to have significant impact on human health.
E. Polat, Süleyman Günay
semanticscholar   +1 more source

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

Partial Least‐Squares Regression

2013
This chapter presents the most widely applied and, probably, satisfactory multivariate regression method used nowadays: partial least squares (PLS). Graphical explanations of many concepts are given to complement the more formal mathematical background. Several approaches to solving current problems are suggested.
José Manuel Andrade‐Garda, Alatzne Carlosena‐Zubieta, Ricard Boqué‐Martí, Joan Ferré‐Baldrich   +3 more
openaire   +1 more source

Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression.

Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy, 2020
The precise and nondestructive detection of leaf chlorophyll content is one key to assessing the health status of crops. The objective of this study was to develop a precision method for determining the leaf chlorophyll content in rape.
Xiaowan Chen, Zhenyu Dong, Jinbao Liu, Huanyuan Wang, Yang Zhang, Tianqing Chen, Yichun Du, Lirong Shao, Jiancang Xie   +8 more
semanticscholar   +1 more source

Kernel Partial Least-Squares Regression

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical ...
null Bai Yifeng, null Xiao Jian, null Yu Long   +2 more
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Spectral Partial Least Squares Regression

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010
Linear Graph Embedding (LGE) is the linearization of graph embedding, and has been applied in many domains successfully. However, the high computational cost restricts these algorithms to be applied to large scale high dimensional data sets. One major limitation of such algorithms is that the generalized eigenvalue problem is computationally expensive ...
Jiangfeng Chen, Baozong Yuan
openaire   +1 more source

Envelopes: A new chapter in partial least squares regression

Journal of Chemometrics, 2020
We describe and elaborate on foundations that connect partial least squares regression with recently developed envelope theory and methodology. These foundations explain why PLS regression can work well in high‐dimensional regressions where the number of
R. D. Cook, L. Forzani
semanticscholar   +1 more source

Microwave characterization using partial least square regression

2016 IEEE Conference on Electromagnetic Field Computation (CEFC), 2016
Inverse problems for determination of dielectric materials properties (complex permittivity) are usually solved by iterative methods using numerically based forward model. These methods are computationally expensive. In this paper, we propose a fast inversion model based on partial least square regression (PLSR).
Sadou, Hakim, Hacib, Tarik, Le Bihan, Yann, Acikgoz, Hulusi, Meyer, Olivier   +4 more
openaire   +2 more sources

Partial least squares regression for graph mining

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS).
Saigo H., Kramer N., Tsuda K.
openaire   +2 more sources

PoLiSh — smoothed partial least-squares regression

Analytica Chimica Acta, 2001
Partial least-squares (PLS) regression is a very widely used technique in spectroscopy for calibration/prediction purposes. One of the most important steps in the application of the PLS regression is the determination of the correct number of dimensions to use in order to avoid over-fitting, and therefore to obtain a robust predictive model.
Douglas N. Rutledge, António Barros, Ivonne Delgadillo   +2 more
openaire   +1 more source