Orthogonal projections to latent structures (O‐PLS)

By Johan Trygg, Svante Wold. 2002

2,361 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Archeology, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/130247eb32329ce54bf895ebcfd4fbc754ef077b

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In Analytical Chemistry

In Archeology

In Biomedical Engineering

Citations in the corpus

Path modelling by sequential PLS regression (2011, methodology): “Note that this method has nothing to do with the O-PLS method [44] which is also based on some type of orthogonalisation.”
Data Pre-processing (2009, neutral): “A slightly different way of approaching the problem was suggested by Trygg and Wold (2002) . ch002.indd 42 11/18/2008 2:15:55 PM Optimized scaling (OS) was introduced by Karstang and Manne (1992) , but has received limited attention.”
Multivariate Analysis in Metabolomics. (2012, methodology): “The predictive OPLS-DA scores and loadings used to estimate Y are then composed of variation directly correlated with Y and free of interfering structured variation, yielding enhanced interpretability when compared with PLS-DA [43, 52].”
A review on machine learning principles for multi-view biological data integration (2016, background): “from X the systematic variation that is not correlated to Y [121].”
Multivariate paired data analysis: multilevel PLSDA versus OPLSDA (2009, methodology): “OPLSDA was introduced as an improvement of the PLSDA method to discriminate two or more groups (classes) using multivariate data (Bylesjo et al. 2006; Trygg and Wold 2002).”
Improving the transfer of near infrared prediction models by orthogonal methods (2009, methodology): “[30] provides more”
Some theoretical properties of the O‐PLS method (2004, methodology): “According to Trygg and Wold [13], O-PLS application has proved to be beneficial in reducing the number of PLS components.”
Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. (2013, neutral): “52,53 This results in a rotation of the standard PLS model so that the variance important for the defined group separation is focused into the predictive components ( x -axis; Fig.”