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Group-wise Partial Least Squares Regression

José Camacho; Edoardo Saccenti
Abstract:
This paper introduces the Group-wise Partial Least Squares (GPLS) regression. GPLS is a new Sparse PLS (SPLS) technique where the sparsity structure is dened in terms of groups of correlated variables, similarly to what is done in the related Group-wise Principal Component Analysis (GPCA). These groups are found in correlation maps derived from the data to be analyzed. GPLS is especially useful for exploratory data analysis, since suitable values for its metaparameters can be inferred upon visualization of the correlation maps. Following this approach, we show GPLS solves an inherent problem of SPLS: its tendency to confound the data structure as a result of setting its metaparameters using standard approaches for optimizing prediction, like cross-validation. Results are shown for both simulated and experimental data.
Research areas:
Year:
2018
Type of Publication:
Article
Journal:
Journal of Chemometrics (Wiley)
Volume:
32
Number:
3
Pages:
1:11
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