Abstract
AbstractA 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.
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