Perspective on essential information in multivariate curve resolution

Abstract We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.

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