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Much faster cross‐validation in PLSR‐modelling by avoiding redundant calculations
Author(s) -
Liland Kristian Hovde,
Stefansson Petter,
Indahl Ulf Geir
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3201
Subject(s) - partial least squares regression , cross validation , computer science , kernel (algebra) , algorithm , lookup table , regression , data mining , mathematics , artificial intelligence , machine learning , statistics , combinatorics , programming language
A novel formulation of the wide kernel algorithm for partial least squares regression (PLSR) is proposed. We show how the elimination of redundant calculations in the traditional applications of PLSR helps in speeding up any choice of cross‐validation strategy by utilizing precalculated lookup matrices. The proposed lookup approach is combined with some additional computational shortcuts resulting in highly effective and numerically accurate cross‐validation results. The computational advantages of the proposed method are demonstrated by comparisons to the classical NIPALS and the bidiag2 algorithms for calculating cross‐validated PLSR models. Problems including both one and several responses, double/nested cross‐validated, and one‐vs‐all classification are among the considered applications.