نتایج جستجو برای: pls partial least squares application

تعداد نتایج: 1339124  

Journal: :Resonance 2021

Partial least square (PLS) analysis is the most favourite tool in chemometrics to develop calibration models. PLS technique allows us decipher even complex systems by analysing all variables instead of looking at them one a time. not only capture maximum variation associated with predictor (i.e. spectra) and predicted concentration) but also maximises correlation between them. The present artic...

2010
William Robson Schwartz Larry S. Davis

Title of dissertation: LOOKING AT PEOPLE USING PARTIAL LEAST SQUARES William Robson Schwartz Doctor of Philosophy, 2010 Dissertation directed by: Professor Larry S. Davis Analysis of images involving humans is of significant interest in computer vision because problems such as detection, modeling, recognition, and tracking are fundamental to model interactions between people and understand high...

In the present work, the simultaneous determination of Brilliant green (BG) and Crystal violet (CV) dyes with overlapped absorption spectra in binary mixture solution, was carreid out using the partial least squares (PLS) and direct ortogonal signal correction-partial least squares (DOSC-PLS) methods. The results obtained indicate that by applying DOSC on the calibration and prediction data for...

1997
June Liu Kwanggi Min Chonghun Han Kun Soo Chang

−The accurate and reliable on-line estimation of product quality is an essential task for successful process operation and control. This paper proposes a new estimation method that extends the conventional linear PLS (Partial Least Squares) regression method to a nonlinear framework in a more robust manner. To handle the nonlinearities, nonlinear PLS based on linear PLS and neural network has b...

Journal: :JCP 2014
Long Xu Jiangang Lu Qinmin Yang Jinshui Chen Yingzi Shi

An objective wavelength selection method is proposed for near-infrared (NIR) spectroscopy mainly to overcome the possible subjectivity introduced by moving window partial least squares regression (MWPLS). This improved procedure (iMWPLS) introduced an indicator to evaluate importance of each wavelength and then all wavelengths were ranked by these indicators. On the basis of the indicator ranki...

1995
Cajo J F Ter Braak

Citation: ter Braak, C. J. F. (1995). Non-linear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (k-nearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches. Abstract Current environmental problems, such as acid rain and global warming, have greatly increased interest in fossil speci...

1993
Cajo J.F. Ter Braak Steve Juggins H. J. B. Birks Hilko Van der Voet

(1993). Weighted averaging partial least squares regression (WA-PLS): definition and comparison with other methods for species-environment calibration. ABSTRACT A multivariate calibration method is proposed that relates to correspondence analysis as partial least squares (PLS) relates to principal components analysis. The new method extends and improves the weighted averaging (WA) method for in...

2013
Kee Siong Ng

Partial Least Squares (PLS) is a widely used technique in chemometrics, especially in the case where the number of independent variables is significantly larger than the number of data points. There are many articles on PLS [HTF01, GK86] but the mathematical details of PLS do not always come out clearly in these treatments. This paper is an attempt to describe PLS in precise and simple mathemat...

Journal: :Briefings in bioinformatics 2007
Anne-Laure Boulesteix Korbinian Strimmer

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as...

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