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

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

Abstract 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 th...

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Bibliographic Details
Main Authors: Geonseok Lee, Kichun Lee
Format: Article
Language:English
Published: BMC 2021-01-01
Series:BioData Mining
Subjects:
Feature selection
PLS
Orthogonal signal correction
Regression vector
Permutation test
Online Access:https://doi.org/10.1186/s13040-021-00240-3

Internet

https://doi.org/10.1186/s13040-021-00240-3

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