ROSA—a fast extension of partial least squares regression for multiblock data analysis. (17th October 2016)

• Record Type: Journal Article
• Authors: Liland, Kristian Hovde; Næs, Tormod; Indahl, Ulf G.
• Is Part Of: Journal of chemometrics. Volume 30:Number 11(2016)
• Page Start: 651
• Page End: 662
• Publication Date: 2016-10-17
• Subjects: Data fusion -- Deflation -- Multiblock -- Orthogonalization -- PLSR; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2824
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract : We present the response‐oriented sequential alternation (ROSA) method for multiblock data analysis. ROSA is a novel and transparent multiblock extension of the partial least squares regression (PLSR). According to a "winner takes all" approach, each component of the model is calculated from the block of predictors that most reduces the current residual error. The suggested algorithm is computationally fast compared with other multiblock methods because orthogonal scores and loading weights are calculated without deflation of the predictor blocks. Therefore, it can work effectively even with a large number of blocks included. The ROSA method is invariant to block scaling and ordering. The ROSA model has the same attributes (vectors of scores, loadings, and loading weights) as PLSR and is identical to PLSR modeling for the case with only one block of predictors. Abstract : A quick data fusion method for many data blocks is proposed as a generalization of partial least squares regression that can also be interpreted as a generalization of variable selection. Improved interpretability is accomplished through a single‐block focus. Stability and speed are ensured through Gram–Schmidt steps and minimal deflations.