Much faster cross‐validation in PLSR‐modelling by avoiding redundant calculations. (5th February 2020)

• Record Type: Journal Article
• Authors: Liland, Kristian Hovde; Stefansson, Petter; Indahl, Ulf Geir
• Other Names: Rajko Robert guestEditor.; Abdollahi Hamid guestEditor.
• Is Part Of: Journal of chemometrics. Volume 34:Number 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-05
• Subjects: classification -- kernel matrix -- regression -- subspace modelling -- validation; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3201
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: 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. Abstract : A novel formulation of partial least squares for wide predictor matrices is presented. It sheds new light on what are the essential building blocks in components extraction. Substantial speed advantages are obtained because of fast lookups and avoiding redundancy in cross‐validation of single‐ and multiple‐response models.