Feature selection using distributions of orthogonal PLS regression vectors in spectral data

Feature selection, which is important for successful analysis of chemometric data, aims to produce parsimonious and predictive models. Partial least squares (PLS) regression is one of the main methods in chemometrics for analyzing multivariate data with input X and response Y by modeling the covaria...

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Detalles Bibliográficos
Autores principales: Lee, Geonseok, Lee, Kichun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821640/
https://www.ncbi.nlm.nih.gov/pubmed/33482872
http://dx.doi.org/10.1186/s13040-021-00240-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821640/
https://www.ncbi.nlm.nih.gov/pubmed/33482872
http://dx.doi.org/10.1186/s13040-021-00240-3

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