Sparse N-way Partial Least Squares by L1-penalization

[EN] N-PLS, as the natural extension of PLS to N-way structures, tries to maximize the covariance between an X and a Y N-way data arrays. It provides a useful framework for fitting prediction models to N-way data. However, N-PLS by itself does not perform variable selection, which indeed can facilitate interpretation in different situations (e.g. the so-called ¿¿omics¿ data). In this work, we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization. The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results. To test Sparse N-PLS performance two different simulated data sets were used, whereas to show its utility in a biological context a real time course metabolomics data set was used.Hervás-Marín, D.; Prats-Montalbán, JM.; Garcia-Cañaveras, J.; Lahoz Rodríguez, AG.; Ferrer, A. (2019). Sparse N-way Partial Least Squares by L1-penalization. Chemometrics and Intelligent Laboratory Systems. 185:85-91. https://doi.org/10.1016/j.chemolab.2019.01.004S859118