Bagging for robust non-linear multivariate calibration of spectroscopy

Wang, Ke; Chen, Tao; Lau, Raymond

doi:10.1016/j.chemolab.2010.10.004

Cited by 35 publications

(20 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the form of covariance function is not restricted to that in equation (1), the only constraint being that it must generate a non-negative definite covariance matrix for any set of data points [13]. The following covariance function is widely used in the literature [16,26,27] and also adopted in this paper:…”

Section: Overview Of Gprmentioning

confidence: 99%

“…The popularity of GPR is partly due to its theoretical link to Bayesian non-parametric statistics [9,17], infinite neural networks [14], kernel methods in machine learning [4,24], and spatial statistics (where it is more widely known as kriging) [8]. In addition, various empirical studies have demonstrated that GPR attains prediction accuracy that is at least comparable to (and in many cases better than) other models such as neural networks [15,21,26,27].…”

Section: Introductionmentioning

confidence: 99%

“…This pragmatic and straightforward approach was taken in many applications (e.g. [7,26,27]), though it is not ideal. A key to modelling multi-response Gaussian processes is the formulation of covariance function that describes not only the correlation between data points, but also the correlation between responses.…”

Section: Introductionmentioning

confidence: 99%

“…In chemometrics and related areas, GPR has been applied to calibration of spectroscopic analysers [6,16,26], response surface modelling [27], dynamic process modelling [11,15], system identification [5] and ensemble learning [12,26], among others. The popularity of GPR is partly due to its theoretical link to Bayesian non-parametric statistics [9,17], infinite neural networks [14], kernel methods in machine learning [4,24], and spatial statistics (where it is more widely known as kriging) [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian process regression with multiple response variables

Wang

Chen

2015

Chemometrics and Intelligent Laboratory Systems

Self Cite

View full text Add to dashboard Cite

Gaussian process regression (GPR) is a Bayesian non-parametric technology that has gained extensive application in data-based modelling of various systems, including those of interest to chemometrics. However, most GPR implementations model only a single response variable, due to the difficulty in the formulation of covariance function for correlated multiple response variables, which describes not only the correlation between data points, but also the correlation between responses. In the paper we propose a direct formulation of the covariance function for multi-response GPR, based on the idea that its covariance function is assumed to be the "nominal" uni-output covariance multiplied by the covariances between different outputs. The effectiveness of the proposed multi-response GPR method is illustrated through numerical examples and response surface modelling of a catalytic reaction process.

show abstract

Section: Overview Of Gprmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gaussian process regression with multiple response variables

Wang

Chen

2015

Chemometrics and Intelligent Laboratory Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…See for example the seminal book [3] and the references therein for details. In chemometrics and related areas, GPR has been applied to a range of problems, such as calibration of spectroscopic analysers [4,5], response surface modelling [6], system identification [7], ensemble learning [5,8], prediction of transmembrane pressure [9], and prediction of percutaneous absorption [10,11], among others.…”

Section: Introductionmentioning

confidence: 99%

Gaussian process regression with functional covariates and multivariate response

Wang

Chen

2017

Chemometrics and Intelligent Laboratory Systems

Self Cite

View full text Add to dashboard Cite

Gaussian process regression (GPR) has been shown to be a powerful and effective nonparametric method for regression, classification and interpolation, due to many of its desirable properties. However, most GPR models consider univariate or multivariate covariates only. In this paper we extend the GPR models to cases where the covariates include both functional and multivariate variables and the response is multidimensional. The model naturally incorporates two different types of covariates: multivariate and functional, and the principal component analysis is used to de-correlate the multivariate response which avoids the widely recognised difficulty in the multi-output GPR models of formulating covariance functions which have to describe the correlations not only between data points but also between responses. The usefulness of the proposed method is demonstrated through a simulated example and two real data sets in chemometrics.

show abstract

Ensemble partial least squares regression for descriptor selection, outlier detection, applicability domain assessment, and ensemble modeling in QSAR/QSPR modeling

Cao

Deng

Zhu

et al. 2017

Journal of Chemometrics

View full text Add to dashboard Cite

In QSAR/QSPR modeling, building an accurate partial least squares (PLS) model usually involves descriptor selection, outlier detection, applicability domain assessment, nonlinear relationship, and model stability problems. In the present study, we presented an ensemble PLS (EnPLS) method for solving these modeling tasks under a unified methodology framework. EnPLS aims at developing a consistent algorithmic framework by means of the idea of ensemble learning and statistical distribution. The approach exploits the fact that the distribution of PLS model coefficients provides a mechanism for ranking and interpreting the effects of variables, whereas the distribution of prediction errors provides a mechanism for differentiating the outliers from normal samples and assessing the applicability domain of models. The use of statistics of these distributions, namely, mean/median value and standard deviation, inherently provides a feasible way to effectively describe the information contained by the original samples. Furthermore, ensemble modeling and prediction based on several cross-predictive PLS models could effectively improve the model prediction performance and increase the model stability to a certain extent. The aqueous solubility data are used to demonstrate the ability of our proposed EnPLS method in solving various modeling tasks such as descriptor selection, outlier detection, applicability domain assessment, performance improvement, and model stability. Finally, a freely available R package implementing EnPLS is developed to facilitate the use of chemists and pharmacologists. The R package is freely available at https://github.com/wind22zhu/enpls1.2. KEYWORDS applicability domain assessment, ensemble learning, outlier detection, partial least squares (PLS), QSAR/ QSPR, variable selection 1 | INTRODUCTIONChemometrics aims at solving chemical problems using statistical or informatics methods. There is a great deal of previous successful research in this filed, revolving around topics such as quantitative structure-activity relationships (QSARs), quantitative structure-property relationships (QSPRs), multivariate calibration, reaction design, and drug design. The QSAR/QSPR methodology aims at finding a model, which allows for correlating the activities to structures within a family of compounds.

show abstract

Bagging for robust non-linear multivariate calibration of spectroscopy

Cited by 35 publications

References 57 publications

Gaussian process regression with multiple response variables

Gaussian process regression with multiple response variables

Gaussian process regression with functional covariates and multivariate response

Ensemble partial least squares regression for descriptor selection, outlier detection, applicability domain assessment, and ensemble modeling in QSAR/QSPR modeling

Contact Info

Product

Resources

About