ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Stepwise Multiblock Latent Variable Regression

4 September 2018, 10:30 – 10:50


Submitted by
Maria Campos
Maria Campos (University of Coimbra, Department of Chemical Engineering), Marco Reis (University of Coimbra, Department of Chemical Engineering)
Methods able to handle multiple blocks of data are attracting increasingly more practitioners, as they are able to model the contribution from the different blocks while retaining their natural structure. Among these multiblock methods, SO-PLS (Sequential Orthogonal-PLS) in particular stands out by its interesting prediction and interpretation capabilities, associated with desirable modelling features such as independence from relative scaling of the different data blocks, and the flexibility to handle blocks with different dimensionalities and pseudo-ranks. However this method becomes cumbersome when a high number of blocks are available as the analysis is critically dependent on the order by which they are incorporated in the model. When no a priori knowledge is available for establishing the order of the blocks or when no order is preferred a priori, SO-PLS faces the problem of having to find the most adequate one through an exhaustive search across all permutations. Furthermore SO-PLS (as well as any other current multiblock method) does not contemplate the possibility for selecting/excluding non-relevant blocks of variables. In this article we present an efficient approach for establishing the optimal order of the blocks in SO-PLS with additional capabilities for block selection/exclusion: stepwise SO-PLS. It is computationally much faster and leads to an optimum or very close to optimum solution regarding the selected blocks and their order. A comparison between Stepwise SO-PLS and current multiblock approaches is presented based on a real case study.

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