Browsing NOFIMA vitenarkiv by Author "Smilde, Age K."
Now showing items 1-7 of 7
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Common and distinct components in data fusion
Smilde, Age K.; Måge, Ingrid; Næs, Tormod; Hankemeier, Thomas; Lips, Miriam B.; Kiers, Henk A. L.; Acar, Evrim; Bro, Rasmus (Journal article; Peer reviewed, 2017)In many areas of science, multiple sets of data are collected pertaining to the same system. Examples are food products that are characterized by different sets of variables, bioprocesses that are online sampled with ... -
Confidence ellipsoids for ASCA models based on multivariate regression theory
Liland, Kristian Hovde; Smilde, Age K.; Marini, Federico; Næs, Tormod (Journal article; Peer reviewed, 2018) -
Critical evaluation of assessor difference correction approaches in sensory analysis
Großmann, Justus L.; Westerhuis, Johan A.; Næs, Tormod; Smilde, Age K. (Others, 2022)In sensory data analysis, assessor-dependent scaling effects may hinder the analysis of product differences. Romano et al. (2008) compared several approaches to reduce scaling differences between assessors by their ability ... -
Performance of methods that separate common and distinct variation in multiple data blocks
Måge, Ingrid; Smilde, Age K.; Van der Kloet, Frans (Journal article; Peer reviewed, 2018) -
Selecting the number of factors in principal component analysis by permutation testing—Numerical and practical aspects
Vitale, Raffaele; Westerhuis, Johan A.; Næs, Tormod; Smilde, Age K.; de Noord, Onno E.; Ferrer, Alberto (Journal article; Peer reviewed, 2017)Selecting the correct number of factors in principal component analysis (PCA) is a critical step to achieve a reasonable data modelling, where the optimal strategy strictly depends on the objective PCA is applied for. In ... -
Selection of principal variables through a modified Gram–Schmidt process with and without supervision
Skogholt, Joakim; Liland, Kristian Hovde; Næs, Tormod; Smilde, Age K.; Indahl, Ulf Geir (Peer reviewed; Journal article, 2023)In various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low-dimensional model solutions. In ... -
Sequential and orthogonalized PLS (SO-PLS) regression for path analysis: Order of blocks and relations between effects
Næs, Tormod; Romano, Rosaria; Tomic, Oliver; Måge, Ingrid; Smilde, Age K.; Liland, Kristian Hovde (Peer reviewed; Journal article, 2020)This paper is about the use of the multiblock regression method sequential and orthogonalized partial least squares (SO-PLS) for path modeling. The paper is a follow up of previously published papers on the same topic and ...