Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models

Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models

Background: For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensio...

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Bibliographic Details
Main Authors: Galindo-Prieto, B. (Author), Geladi, P. (Author), Trygg, J. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
article
big data
Big data
data analysis
Data Analysis
data integration
Data integration
data mining
Data mining
Data Mining
Data streams
Dimensionality reduction
feature selection
Feature selection
food industry
Interpretability
Latent variable interpretation
Matrix algebra
MB-VIOP
mining
Model components
Multiblock analysis
Multiblock variable selection
multiomics
Multivariant analysis
multivariate analysis
Multivariate Analysis
Multivariate data analysis
OnPLS
Orthogonal projection
reliability
reproducibility
Reproducibility of Results
Variable importance in multiblock regression
Variable importances
Variable influence on projection
Variable selection
Variable selection methods
VIP
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