An algorithm for robust multiblock partial least squares predictive modelling

Puneet Mishra*, Kristian Hovde Liland

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

A new algorithm for robust multiblock (data fusion) modelling in the presence of outlying observations is presented. The method is a combination of a robust modelling technique called iterative reweighted partial least squares and the block order and scale-independent component-wise multiblock partial least squares modelling. The method is based on automatic down-weighting of outlying observations such that their contribution is minimal during the estimation of block-wise partial least squares models, thus leading to robust modelling minimally affected by outliers. The algorithm and test of the methods for modelling multiblock data sets (simulated and real) in the presence of outlying observation are demonstrated.

Original languageEnglish
Article numbere3480
JournalJournal of Chemometrics
Volume37
Issue number6
DOIs
Publication statusPublished - Jun 2023

Keywords

  • data fusion
  • multiblock
  • multivariate
  • robustness
  • spectroscopy

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