Swiss knife covariates selection: A unified algorithm for covariates selection in single block, multiblock, multiway, multiway multiblock cases including multiple responses. (8th September 2022)

• Record Type: Journal Article
• Authors: Mishra, Puneet; Liland, Kristian Hovde; Indahl, Ulf Geir
• Is Part Of: Journal of chemometrics. Volume 36:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-08
• Subjects: feature selection -- multivariate -- multiway -- multiblock; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3441
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: A novel unified covariates selection algorithm called Swiss knife covariates selection (SKCovSel) is presented. It is suitable for selecting covariates in a wide range of data scenarios such as a single two‐way data block, two‐way multiblock, multiway, multiway multiblock, selection of covariates along different modes for multiway data blocks and for selecting covariates for all mentioned cases in multiple response scenarios. In the multiblock case, the method can be scale and data block order‐independent depending on the preference of the user. For multiway scenarios, the method can be multiway mode order independent, depending on the preference of the user. The proposed SKCovSel algorithm generalises the recent speed improvements from faster CovSel to all mentioned data block cases. It also reformulates the multiway case to do proper deflation and rank one slab selections. Particularly, for modelling of multiblock data sets, the SKCovSel follows the "winner takes all" strategy of the stepwise response‐oriented sequential alternation modelling. In the case of multiway data, the SKCovSel strategy considers multiway loading weights after decomposition of a high‐dimensional squared covariance matrix to select features across different modes. The algorithmic steps of the methods are presented, and cases of modelling different data types such as single block, multiblock, multiway multiblock, modes selection for multiway data and multiple responses modelling are shown. The method incorporates all popular covariates selection algorithms existing in the chemometric literature. Abstract : Swiss knife covariates selection is a novel algorithm to select features in data sets of any dimensionality and multiple sources. The method incorporates all popular covariates selection algorithms existing in the chemometric literature in a single algorithm. Due to the non‐deflating nature of the algorithm, any covariates selection modelling carried out with the new algorithm will be natural faster to execute. The algorithm provides regression coefficients for selected features to support feature selective predictive modelling.