Feature selection using distributions of orthogonal PLS regression vectors in spectral data
Feature selection using distributions of orthogonal PLS regression vectors in spectral data
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Publisher
England: BioMed Central Ltd
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Language
English
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England: BioMed Central Ltd
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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 covariance structure in the X and Y spaces. Recently, ort...
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Feature selection using distributions of orthogonal PLS regression vectors in spectral data
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TN_cdi_doaj_primary_oai_doaj_org_article_fb1e9c0c6b874d99a78870d257e2f3ec
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fb1e9c0c6b874d99a78870d257e2f3ec
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ISSN
1756-0381
E-ISSN
1756-0381
DOI
10.1186/s13040-021-00240-3
